AI Engineer – Python, LLM/GenAI, RAG, AWS & AI Agents

Synechron Technologies Pvt. Ltd._INDIA Company; Mhalunge; Presencial; INR 2.500.000 - 4.500.000; Jornada completa

Descripción de la vacante

Synechron Technologies Pvt. Ltd. is seeking an AI Engineer to design and implement AI agents and architectures within CI/CD workflows on AWS. The role emphasizes production-ready GenAI solutions, RAG pipelines, and secure AI automation using Python, LLMs, and AWS services.

The ideal candidate will have hands-on experience with SageMaker, Lambda, vector databases, and tool integrations, contributing to scalable AI capabilities and faster software delivery.

Formación

• Minimum 3 years of experience with LLMs and GenAI.
• Experience designing and implementing RAG pipelines.
• Experience with AWS services for AI deployments.
• Experience building agentic workflows or autonomous AI agents.
• Exposure to code generation and remediation using LLMs.
• Experience with vector databases and embedding generation.

Responsabilidades

• Design, develop, and deploy AI agents and AI architecture within CI/CD workflows on AWS.
• Build production-ready generative AI applications using LLMs (OpenAI, Bedrock, etc.).
• Develop RAG pipelines using SageMaker, Lambda, vector databases, and retrieval components.
• Integrate AI solutions with CI/CD tools, security tools, and monitoring frameworks.
• Collaborate with engineering, DevOps, and security teams to define requirements and ensure secure AI deployments.

Conocimientos

Python

LLMs

RAG pipelines

AWS

CI/CD

Security

Vector databases

Agentic workflows

Code generation

Herramientas

SageMaker

Lambda

Docker

Kubernetes

S3

API Gateway

Descripción del empleo

Job Summary Synechron is seeking an AI Engineer to build and implement AI agents and AI architecture within CI/CD workflows on AWS. The role will focus on developing production-ready generative AI solutions, retrieval-augmented generation (RAG) pipelines, agentic workflows, model integrations, and AI-enabled automation. The AI Engineer will contribute to the design, development, deployment, and continuous improvement of AI solutions using Python, large language models (LLMs), AWS services, vector databases, and secure engineering practices. The role will also support code generation, code fixing, security automation, and remediation systems where applicable. The position contributes to business objectives by accelerating software delivery, improving engineering productivity, enabling secure AI adoption, automating technical processes, and delivering scalable AI capabilities through reliable cloud-based architectures.

Software Requirements

• Python: Strong professional experience in Python and AI development, including application design, API development, automation, testing, and production deployment.
• LLMs and Generative AI: Minimum 3 years of experience working with LLMs and GenAI, including OpenAI, Bedrock, or similar technologies.
• RAG pipeline engineering: Hands‑on experience designing and implementing RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document processing, retrieval, and response generation.
• MCP server development: Experience developing MCP servers and integrating tools, services, APIs, and data sources with AI applications.
• Agentic workflows: Experience building agentic workflows or autonomous AI agents, including task planning, tool use, orchestration, memory, workflow control, and response validation.
• Code generation and code fixing using LLMs: Exposure to using LLMs for code generation, code review, code transformation, defect identification, and code remediation.
• Vector databases: Experience with vector databases, embedding generation, similarity search, metadata filtering, indexing, and retrieval optimization.
• AWS services: Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway and SageMaker.
• Application security: Understanding of application security principles, common vulnerabilities, secure coding practices, identity and access controls, and protection of AI applications and data.
• CI/CD integration: Experience implementing or integrating AI agents and AI architecture within CI/CD workflows on AWS.

Preferred Software Skills

• Exposure to CI/CD tools such as GitHub, Jenkins, Harness, or equivalent tools.
• Experience with security tools such as Snyk, SonarQube, or equivalent tools.
• Knowledge of containerisation and orchestration using Docker and Kubernetes.
• Prior work in security automation or remediation systems.
• Experience with automated testing, code quality checks, vulnerability scanning, and deployment gates.
• Experience integrating AI solutions with enterprise APIs, repositories, issue management platforms, and security tools.

Overall Responsibilities

• Design, develop, and implement AI agents and AI architecture within CI/CD workflows on AWS.
• Build and maintain production‑ready generative AI applications using LLMs, including OpenAI, Bedrock, or similar technologies.
• Develop RAG pipelines using SageMaker, Lambda, vector databases, embeddings, document ingestion, retrieval, and response‑generation components.
• Develop MCP servers and integrate tools, APIs, services, data sources, and enterprise systems with AI applications.
• Build agentic workflows and autonomous AI agents that can plan tasks, use approved tools, manage context, and produce validated outcomes.
• Apply Python engineering practices to create maintainable, testable, observable, and scalable AI solutions.
• Support code generation and code fixing using LLMs, including automated code recommendations, remediation workflows, and developer productivity capabilities.
• Integrate AI solutions with CI/CD tools, source control, security tools, testing frameworks, and deployment workflows.
• Use AWS services, including Lambda, ECS/EKS, S3, API Gateway, and SageMaker, to deploy and operate AI applications.
• Apply application security principles to AI architecture, data handling, API access, model interactions, prompts, tools, and generated code.
• Evaluate AI solution performance using measurable criteria such as accuracy, relevance, latency, reliability, cost, security, and operational stability.
• Collaborate with engineering, DevOps, security, architecture and business stakeholders to define requirements and deliver AI capabilities.
• Contribute to sustainable engineering practices by optimising model usage, compute consumption, storage, retrieval processes and cloud resource utilisation.

Performance Outcomes and Expectations

• AI agents and AI architecture are implemented within defined CI/CD workflows on AWS.
• RAG pipelines provide relevant, reliable, secure and traceable responses based on approved data sources.
• AI agents use tools and services according to defined permissions, validation controls and operational policies.
• AI solutions meet agreed performance, security, scalability, cost and maintainability criteria.
• Code generation and code‑fixing capabilities improve development productivity while maintaining appropriate review and quality controls.
• Vulnerabilities and security risks are identified, documented, prioritised and addressed through appropriate automation or remediation processes.
• AI applications are supported by appropriate testing, monitoring, documentation and operational procedures.

Technical Skills (By Category)

Programming Languages Essential

• Strong experience with Python for AI development, automation, API integration, data processing and cloud‑based application development.
• Ability to write maintainable, testable, modular and secure Python code.
• Experience developing services, scripts, integrations and automation for LLM and RAG applications.
• Preferred: Experience with JavaScript or TypeScript for AI interfaces, service integrations or tool development.
• Experience with shell scripting for automation and CI/CD activities.
• Experience working with multiple programming languages in enterprise software environments.

Databases and Data Management

• Experience with vector databases and vector search.
• Understanding of embeddings, chunking, indexing, similarity search, metadata filtering and retrieval optimisation.
• Experience designing RAG data flows, including document ingestion, preprocessing, storage, retrieval and response grounding.
• Understanding of data security, access controls, retention and protection of sensitive information used by AI applications.
• Preferred: Experience with relational and NoSQL databases.
• Experience with managed vector database services and hybrid search.
• Knowledge of data quality, data lineage, document versioning and source validation.
• Experience implementing data ingestion and synchronisation pipelines.

Cloud Technologies

• Good knowledge of AWS services, including Lambda, ECS/EKS, S3, API Gateway and SageMaker.
• Experience deploying AI applications and services in AWS environments.
• Understanding of AWS identity and access management, networking, logging, monitoring, scalability and cost considerations.
• Experience implementing AI agents and AI architecture within AWS‑based CI/CD workflows.
• Preferred: Experience with additional AWS services for observability, messaging, secrets management, databases and workflow orchestration.
• Experience with serverless and event‑driven architectures.
• Experience implementing multi‑environment deployment strategies.
• Experience optimising cloud resource usage, model inference costs, storage and compute consumption.

Frameworks and Libraries

• Experience with LLM and GenAI technologies, including OpenAI, Bedrock or similar platforms.
• Experience developing RAG pipelines using SageMaker and Lambda.
• Experience with agentic workflows or autonomous AI agents.
• Experience developing MCP servers and tool integrations.
• Experience with Python libraries and frameworks for AI, data processing, APIs, testing and automation.
• Understanding of prompt design, context management, response validation and model evaluation.
• Preferred: Experience with AI agent orchestration frameworks.
• Experience with model evaluation, tracing, observability and prompt/version management.
• Experience with automated code generation, code fixing, code analysis and software remediation using LLMs.
• Experience integrating AI applications with enterprise systems, APIs, repositories, security platforms and workflow tools.

Development Tools and Methodologies

• Experience integrating AI agents and AI architecture into CI/CD workflows.
• Understanding of source control, branching, pull requests, code reviews, automated testing and deployment practices.
• Experience with Python‑based software development lifecycle practices.
• Understanding of application architecture, API design, distributed systems and cloud‑native development.
• Ability to document AI architecture, data flows, agent behaviour, tool permissions, dependencies and operational procedures.
• Experience applying quality controls to AI‑generated code and AI‑enabled development processes.
• Preferred: Exposure to CI/CD tools such as GitHub, Jenkins, Harness or equivalent tools.
• Experience with security tools such as Snyk, SonarQube or equivalent tools.
• Knowledge of Docker and Kubernetes.
• Experience with security automation or remediation systems.
• Experience with automated vulnerability scanning, software composition analysis, static analysis and security gates.
• Experience with DevSecOps, infrastructure automation and release governance.

Security Protocols

• Understanding of application security principles and common application vulnerabilities.
• Knowledge of secure coding, input validation, output validation, dependency management and secrets protection.
• Understanding of authentication, authorisation, identity and access management and least‑privilege controls.
• Ability to secure LLM applications, RAG pipelines, MCP servers, APIs, prompts, tools and generated code.
• Awareness of risks such as prompt injection, data leakage, insecure tool execution, excessive permissions, malicious content and untrusted model output.
• Experience applying security reviews, vulnerability assessment and remediation practices to AI and software solutions.
• Preferred: Experience using Snyk, SonarQube or equivalent security and code quality tools.
• Prior work in security automation or remediation systems.
• Experience integrating security checks into CI/CD pipelines.
• Knowledge of AI security testing, model risk controls, data masking and audit logging.

Experience Requirements

• Minimum 8+ years of overall professional experience in software engineering, cloud engineering, AI engineering, data engineering or related technology roles.
• Minimum 3+ years of AI experience, including hands‑on work with LLMs, GenAI, Python, RAG pipelines, AI agents or related AI application development.
• Demonstrated experience building and implementing AI agents and AI architecture within CI/CD workflows on AWS.
• Hands‑on experience with LLMs and GenAI, including OpenAI, Bedrock or similar technologies.
• Experience with RAG pipeline engineering using SageMaker, Lambda, vector databases, embeddings and retrieval systems.
• Experience with MCP server development, tool integrations, agentic workflows or autonomous AI agents.
• Experience with AWS services including Lambda, ECS/EKS, S3 and API Gateway.
• Experience applying application security principles to software, cloud, AI, API and data solutions.
• Preferred experience includes CI/CD tools, security tools, Docker, Kubernetes, security automation and remediation systems.
• Candidates may qualify through equivalent professional experience, demonstrable production AI implementations, substantial open‑source contributions, technical training or a combination of relevant experience and applied project delivery.

Day‑to‑Day Activities

• Design, develop, test and deploy AI agents, RAG pipelines, MCP servers and LLM‑based applications using Python and AWS services.
• Collaborate with engineering, DevOps, security, architecture and business stakeholders to define requirements, review designs and resolve implementation issues.
• Integrate AI solutions with CI/CD workflows, vector databases, application programming interfaces, code repositories, security tools and remediation systems.
• Evaluate solution performance, security, reliability, latency, cost and response quality while making technical decisions within approved architecture and governance standards.
• Integrate AI solutions with existing ecosystems where analogous for continuous integration.

Qualifications

• A bachelor’s or master’s degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, Data Science or a related field is preferred; equivalent relevant experience may be considered.
• Minimum 8+ years of overall professional experience and at least 3+ years of relevant AI experience are required.
• AWS, AI, machine learning, cloud architecture, security or related certifications are preferred but not required where equivalent practical experience is demonstrated.
• Role‑relevant training in LLMs, GenAI, RAG engineering, AI agents, AWS, application security, CI/CD and responsible AI practices is preferred.
• Continuous professional development is expected through technical training, certifications, practical experimentation, knowledge sharing and evaluation of emerging AI and cloud technologies.

Professional Competencies

• Applies structured reasoning, experimentation and evidence‑based problem‑solving to address AI, software, cloud, security and integration challenges.
• Works effectively with engineering, DevOps, security, architecture and business teams to deliver reliable AI solutions and share technical knowledge.
• Communicates AI architecture, risks, limitations, technical decisions and delivery progress clearly to technical and non‑technical stakeholders.
• Adapts to changes in LLM capabilities, AWS services, AI frameworks, security requirements, application architectures and delivery priorities.
• Evaluates innovative AI approaches while balancing accuracy, reliability, security, privacy, maintainability, sustainability and cost.
• Plans and prioritises development, testing, integration, documentation, incident resolution and continuous improvement activities according to business impact and delivery commitments.

SYNECHRON’S DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and an affirmative action employer.

Our DEI initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all.

We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company.

We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations or disabilities to apply.

We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programmes and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status or any other characteristic protected by law.

Employees play a key role in driving our sustainability initiatives, from reducing our carbon footprint to fostering ethical and sustainable business practices across global operations.

All positions are required to adhere to our Sustainability and Health Safety standards, demonstrating a commitment to environmental stewardship, workplace safety and sustainable practices.