Artificial Intelligence Engineer

HTC Global Services; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

HTC Global Services in Hyderabad seeks an experienced AI engineer to design, provision, and manage scalable AI infrastructure on AWS for AI/ML applications across development, testing, and production.

You will deploy and troubleshoot models, implement monitoring with CloudWatch, and maintain CI/CD pipelines, using Terraform/Scalr, Docker, and Kubernetes to ensure scalability and reliability in enterprise environments.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, AI or related field.
• Hands-on experience in Python and software development for AI/ML, data engineering, or cloud-based applications.
• Experience in Machine Learning/Data Science, including training, experimentation, evaluation, and deployment.
• Experience in LLMs/Generative AI use cases such as prompt engineering, RAG, embeddings, vector databases, evaluation frameworks, and guardrails.
• Strong experience with Prompt Engineering including design patterns, chaining, context-window optimization, and enterprise prompting.
• Strong understanding of Agentic AI patterns with multi-step reasoning and orchestration.
• Strong experience with AWS cloud services for building and managing AI/ML apps.
• Hands-on experience with Terraform and/or Scalr for IaC.
• Experience with APIs, microservices, containerized deployments in enterprise settings.
• Experience with SQL and NoSQL databases, vector databases, and data integration.

Responsibilities

• Design, provision, configure, and maintain scalable AI infrastructure on AWS for AI/ML apps across development, testing, and production environments.
• Deploy, manage, and troubleshoot AI/ML models, LLM apps, inference endpoints, and APIs in production.
• Implement monitoring, logging, alerting, and observability with AWS CloudWatch for performance and uptime.
• Provide L2/L3 production support, incident investigation, RCAs, and preventive actions.
• Manage cloud provisioning with Terraform/Scalr, ensuring secure, repeatable deployments.
• Troubleshoot infra, apps, networking, API integrations, containers, and model inference issues.
• Implement and maintain CI/CD pipelines for automated AI deployment, testing, versioning, releases, and rollbacks.
• Monitor AI workloads for latency, throughput, resource use, error rates, and cost; optimize accordingly.
• Support Docker/Kubernetes-based apps, including EKS/ECS, for scalability and reliability.
• Maintain runbooks, incident reports, and disaster recovery docs with cross-team coordination.

Skills

Python

AI/ML

LLMs

Prompt Engineering

AWS

Terraform

Kubernetes

API/Microservices

CI/CD

SQL/NoSQL

Data Engineering

Education

Bachelor's degree in Computer Science or related

Tools

Docker

Kubernetes

Terraform

Scalr

LangChain

LangGraph

LlamaIndex

Hugging Face

OpenAI APIs

Amazon Bedrock

MLflow

Airflow

Spark

Job description

We are seeking a highly skilled AI engineers with Infra and support experience..

Responsibilities include but are not limited to:

Design, provision, configure, and maintain scalable AI infrastructure on AWS to support AI/ML applications across development, testing, and production environments.

• Deploy, manage, and troubleshoot AI/ML models, LLM applications, inference endpoints, and APIs in production environments.
• Implement monitoring, logging, alerting, and observability for AI applications using AWS CloudWatch and other monitoring tools to ensure system availability and performance.
• Provide L2/L3 production support for AI applications, investigate incidents, perform root cause analysis, and implement corrective and preventive actions.
• Manage cloud infrastructure provisioning and configuration using Terraform/Scalr, ensuring secure, consistent, and repeatable deployments.
• Troubleshoot infrastructure, application, networking, API integration, containerization, and model inference issues to minimize service disruptions.
• Implement and maintain CI/CD pipelines for automated AI application deployment, testing, version control, release management, and rollback procedures.
• Monitor AI workloads for latency, throughput, resource utilization, error rates, and operational costs, implementing performance and cost optimization measures.
• Support containerized AI applications using Docker and Kubernetes, including Amazon EKS/ECS, with a focus on scalability, availability, and reliability.
• Maintain operational runbooks, incident reports, support documentation, and disaster recovery procedures while collaborating with DevOps, platform, security, and application support teams to meet SLAs.

Required Skills

• Bachelor’s degree in computer science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
• hands-on experience in Python and software development for AI, ML, data engineering, or cloud-based applications.
• experience in Machine Learning / Data Science, including feature engineering, model training, experimentation, evaluation, and deployment.
• experience in LLMs / Generative AI use cases such as prompt engineering, prompt optimization, RAG, embeddings, vector databases, evaluation frameworks, and guardrails.
• Strong experience with Prompt Engineering, including prompt design patterns, prompt chaining, instruction design, context-window optimization, role/task prompting, structured response generation, and prompt tuning for enterprise use cases.
• Strong understanding of Agentic AI patterns, including multi-step reasoning workflows, tool usage, memory/context handling, orchestration frameworks, and autonomous task execution.
• Strong experience with AWS cloud services for developing, deploying, and managing scalable AI/ML and data-driven applications.
• Hands-on experience with Terraform and/or Scalr for infrastructure as code and environment provisioning.
• Experience with APIs, microservices, and containerized deployments in enterprise environments.
• Experience with SQL and NoSQL databases, vector databases, and data integration across structured and unstructured sources.
• Strong knowledge of software engineering fundamentals including version control, testing, CI/CD, secure coding, code reviews, and release management.
• Experience presenting technical solutions, results, and recommendations to business stakeholders and leadership.
• Strong analytical thinking, problem-solving, communication, and cross-functional collaboration skills.

Preferred Skills

• Experience in financial services, investment management, compliance, risk, or other regulated enterprise environments.
• Experience with MLOps / LLMOps tools and practices for deployment, monitoring, evaluation, and lifecycle management of AI systems.
• Familiarity with frameworks and tools such as LangChain, LangGraph, LlamaIndex, Hugging Face, OpenAI APIs, Amazon Bedrock, MLflow, Airflow, Spark, or similar technologies.
• Experience designing and optimizing prompts for RAG workflows, agentic systems, tool calling, reasoning tasks, workflow automation, and domain-specific enterprise AI applications.
• Experience with semantic search, vector retrieval, document intelligence, conversational AI, and knowledge-grounded AI systems.
• Knowledge of Responsible AI, model governance, explainability, bias detection, risk controls, and enterprise AI safety practices.
• Experience working in Agile / Scrum delivery models.
• Experience leading or mentoring teams and driving implementation across multiple stakeholders.
• Advanced expertise in AI/LLMs with strong hands-on experience in end-to-end model development and deployment.
• Solid data engineering background with experience in building and managing complex, large-scale data systems.
• Proven ability to work effectively in large, collaborative teams within fast-paced, enterprise environments.
• Strong ownership and accountability for end-to-end delivery
• Ability to translate ambiguous business problems into practical AI solutions
• Strong collaboration across engineering, product, business, and governance teams
• Technical leadership in solution design and implementation
• Strong communication and stakeholder management skills
• Continuous learning mindset with focus on emerging AI technologies and best practices