Agentic AI and Generative AI Engineers

Goavega Software India; Bengaluru; Hybrid; INR 3,000,000 - 6,000,000; Full time

Job summary

Goavega Software India is seeking an experienced AI Software Engineer to architect and deliver production-grade AI agents and cloud-native applications. You will design retrieval-augmented generation pipelines, implement guardrails, and optimize performance across distributed systems.

The role requires strong Python, REST API, and cloud experience, with hands-on work on AI frameworks like Semantic Kernel, LangGraph, or LangChain.

Qualifications

• Bachelor's or master's in Computer Science, Engineering, AI, Data Science or related field.
• 8+ years of software development with AI/LLM tech.
• Strong Python programming with OOP, async, testing and performance.
• Experience designing REST APIs and integration using JSON payloads.
• Experience building production-grade AI agents with Semantic Kernel, LangGraph, or LangChain.
• Experience with agent orchestration and multi-agent patterns.
• Knowledge of agent state, memory, context, checkpointing, recovery for long-running workflows.
• Experience in evaluating AI models for quality, safety and latency.

Responsibilities

• Deliver secure, reliable AI software for production environments.
• Design cloud-native AI apps and agentic workflows using Python, REST APIs, and LLMs.
• Architect agent-based solutions with appropriate orchestration and multi-agent patterns.
• Build Retrieval-Augmented Generation pipelines with ingestion, embeddings, indexing and retrieval.
• Develop human-in-the-loop workflows for review, escalation, and feedback.
• Implement guardrails for prompt injection, data privacy, and safe failure handling.
• Optimize context usage, token consumption, latency, and cost while maintaining quality.
• Collaborate with teams to define technical solutions and delivery plans.

Skills

Python programming

REST APIs design

AI Agents & LangChain

Agent orchestration

Context management

Prompt engineering

RAG pipelines

AWS/GCP cloud

Docker & CI/CD

Security & privacy

Education

Bachelor's/Master's in CS/Engineering/AI/Data Science

Tools

Semantic Kernel

LangGraph

LangChain

Vector databases

OpenAI/LLMs

Terraform

Kubernetes

GitHub Actions

Job description

• Bachelors or masters degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
• 8+ years of software development experience building enterprise-grade, customer-facing products and services, including at least 2 years of hands-on experience developing solutions using AI Agents, Skills, or Large Language Model technologies.
• Strong hands-on programming experience with Python, including object-oriented programming, asynchronous programming, concurrency, error handling, testing, packaging, and performance optimisation.
• Experience designing and implementing REST APIs and integrating internal and external services using structured JSON payloads, schemas, authentication, retries, rate limits, and resilient error-handling patterns.
• Experience building production-grade AI agents using frameworks such as Semantic Kernel, LangGraph, or LangChain.
• Experience with agent orchestration and multi-agent architecture patterns, including planning, tool selection, task delegation, workflow coordination, and communication between agents.
• Strong understanding of agent state, memory, context management, session management, checkpointing, and recovery for long-running or multi-step agent workflows.
• Experience designing and implementing agent evaluation frameworks, including quality, task completion, groundedness, safety, latency, reliability, and cost evaluations.
• Experience implementing AI guardrails, including input and output validation, prompt injection protection, access control, content filtering, structured responses, tool-use restrictions, and safe failure handling.
• Strong understanding of Large Language Models and transformer architectures, including attention mechanisms, tokenisation, context windows, model inference, and common model limitations.
• Experience with prompt engineering techniques such as system prompt design, few-shot prompting, structured prompting, tool or function calling, prompt templates, and prompt versioning.
• Experience designing and implementing Retrieval-Augmented Generation pipelines, including document ingestion, parsing, chunking, metadata enrichment, embedding generation, indexing, retrieval, re-ranking, citation, and response generation.
• Experience working with embeddings and vector databases or vector search platforms, including index design, metadata filtering, hybrid search, semantic search, and retrieval quality optimisation.
• Experience with context management and token optimisation techniques, including context selection, summarisation, compression, caching, prompt budgeting, and management of conversation history.
• Experience designing human-in-the-loop workflows for review, approval, exception handling, feedback collection, and escalation of sensitive or low-confidence AI decisions.
• Experience with model customisation techniques such as fine-tuning, instruction tuning, parameter-efficient fine-tuning, or model adaptation, along with the ability to determine when fine-tuning is appropriate compared with prompting or RAG.
• Experience with AWS or GCP and their core compute, storage, networking, identity, security, logging, and monitoring services.
• Hands-on experience with Amazon Bedrock or equivalent managed Generative AI platforms, including model integration, inference configuration, security, observability, and cost management.
• Experience containerising and deploying applications using Docker, with a sound understanding of container security, configuration, networking, resource management, and deployment fundamentals.
• Experience implementing CI/CD workflows using GitHub Actions or equivalent platforms for automated build, test, security validation, packaging, and deployment.
• Experience with AI and model observability, including monitoring of latency, availability, token consumption, cost, retrieval quality, model responses, evaluation scores, safety events, and production drift.
• Strong knowledge of software architecture, design patterns, data structures, algorithms, distributed systems, and cloud-native application design.
• Experience with test-driven development and automated testing using frameworks such as pytest, including unit, integration, API, workflow, evaluation, and end-to-end testing.
• Strong understanding of security, privacy, responsible AI, data governance, and secure software development practices for enterprise AI applications.
• Excellent problem-solving, analytical, debugging, and troubleshooting skills, with the ability to diagnose issues across application, AI model, agent, data, retrieval, and cloud infrastructure layers.
• Strong communication and collaboration skills, with the ability to explain complex AI and software architecture decisions to technical and non-technical stakeholders.
• High energy, a positive attitude, and a strong sense of ownership for delivering reliable and high-quality products.

Below is the list of preferred experience:

• Experience developing enterprise applications using Java or Go.
• Experience with multiple agentic AI frameworks, particularly Semantic Kernel, LangGraph, and LangChain.
• Experience implementing Model Context Protocol-based integrations or similar standards for connecting AI agents with tools, data sources, and enterprise systems.
• Experience with vector databases or vector-enabled data platforms such as Amazon OpenSearch Service, PostgreSQL with pgvector, Pinecone, Weaviate, Milvus, or equivalent technologies.
• Experience with orchestration and deployment platforms such as Kubernetes, Amazon ECS, Amazon EKS, Google Kubernetes Engine, or serverless compute services.
• Experience with model and experiment tracking, prompt management, evaluation platforms, tracing, and LLM observability tools.
• Experience designing event-driven or asynchronous systems using messaging and streaming technologies such as Kafka, RabbitMQ, Amazon SQS, Amazon SNS, or Google Pub/Sub.
• Experience working with relational and NoSQL databases, including PostgreSQL, MySQL, MongoDB, DynamoDB, or equivalent technologies.
• Familiarity with responsible AI practices covering fairness, transparency, explainability, privacy, human oversight, and risk management.
• Experience improving AI application performance through semantic caching, request batching, streaming, model routing, fallback strategies, and inference cost optimisation.
• Experience with infrastructure-as-code technologies such as Terraform, AWS CloudFormation, AWS CDK, or equivalent tools.
• Experience working in a SaaS product environment with multi-tenant architecture, enterprise identity, authorisation, auditability, and data-isolation requirements.

Key Responsibilities

• Ensure the timely delivery of secure, reliable, and high-quality AI software that meets business requirements and maintains predictable performance in production environments.
• Design and develop cloud-native AI applications, agentic workflows, and reusable AI platform capabilities using Python, REST APIs, Large Language Models, and cloud services.
• Architect agent-based solutions using Semantic Kernel, LangGraph, LangChain, or related frameworks, selecting appropriate orchestration, state, memory, context, and multi-agent patterns.
• Design and implement Retrieval-Augmented Generation pipelines covering ingestion, document processing, chunking, embeddings, vector indexing, retrieval, re-ranking, grounding, citation, and response generation.
• Develop human-in-the-loop workflows that support review, approval, intervention, feedback, exception handling, and escalation for business-critical AI processes.
• Establish evaluation strategies and measurable quality criteria for agents, prompts, models, tools, and RAG pipelines, and integrate automated evaluations into the engineering lifecycle.
• Implement guardrails and security controls to reduce risks associated with prompt injection, sensitive data exposure, hallucinations, unauthorised tool usage, unsafe responses, and excessive agent autonomy.
• Optimise context usage, token consumption, latency, throughput, retrieval quality, model selection, and infrastructure cost while maintaining solution quality and reliability.
• Build and maintain REST APIs, asynchronous services, tool integrations, structured outputs, JSON schemas, and connectors required by AI agents and enterprise applications.
• Implement production monitoring and observability for AI applications, including application metrics, distributed traces, model interactions, agent execution paths, token usage, cost, evaluation outcomes, and safety events.
• Develop and maintain CI/CD pipelines using GitHub Actions or equivalent platforms, incorporating automated testing, AI evaluations, dependency scanning, container scanning, policy checks, and deployment validation.
• Deploy and operate AI services on AWS or GCP using appropriate containerised, managed, or serverless deployment patterns.
• Provide technical leadership and mentorship to engineers, guiding architecture, implementation, testing, debugging, secure development, and operational best practices.
• Conduct thorough design and code reviews, ensuring that implementations meet software quality, security, testability, observability, maintainability, and responsible AI standards.
• Collaborate closely with engineering teams, product managers, architects, security teams, data specialists, and stakeholders to define technical solutions, delivery plans, success criteria, and operational requirements.
• Evaluate AI models, frameworks, tools, and cloud services through structured prototypes and technical assessments, and recommend technologies based on quality, security, scalability, operability, and cost.
• Troubleshoot complex production issues across agents, prompts, models, tool integrations, RAG pipelines, vector indexes, APIs, containers, and cloud infrastructure.
• Communicate technical decisions, progress, risks, trade-offs, and recommended solutions clearly to technical and non-technical stakeholders.
• Stay current with developments in Generative AI, agentic frameworks, Large Language Models, cloud AI services, evaluation methodologies, and responsible AI, applying relevant advances to improve product capabilities and engineering practices.