Agentic AI Engineer

Senior Associate – Agentic AI Engineer

Role: Senior Associate – Agentic AI Engineer

Level: Senior Associate

Tower: AI Engineering & Intelligent Automation (AI Managed Services)

Experience: 5–10 years

Key Skills: Agentic AI Workflow Development; LLM Orchestration; Python Engineering; API & Microservices; Cloud-Native AI Platforms (AWS Preferred); AI Guardrails & Evaluation

Educational Qualification:

Bachelor’s degree in Computer Science, Engineering, or related field (Master’s or relevant cloud/AI certifications preferred)

Work Location: Bangalore and Hyderabad (based on your preference)

Job Description

As a Senior Associate – Agentic AI Engineer, you will design, build, and operationalize agentic AI solutions using modern LLM orchestration frameworks and cloud-native architectures. You will work closely with senior engineers, architects, and operations teams to develop scalable, secure, and governed AI workflows that move reliably from development into production.

This role is hands-on and engineering-focused, with responsibilities spanning agent design, orchestration, evaluation, and release readiness within an enterprise AI managed services environment.

Key Responsibilities

Agentic AI Workflow Development

• Design and implement agentic AI workflows using frameworks such as LangGraph, CrewAI, AutoGen, and similar agent orchestration patterns.
• Build multi-agent systems that coordinate reasoning, tool use, memory, and task execution across complex workflows.
• Implement MCP (Model Context Protocol) tools and custom tool interfaces to extend agent capabilities.

LLM Orchestration & Prompt Engineering

• Orchestrate LLM interactions using LangChain and related frameworks across retrieval, tools, memory, and agents.
• Design, test, and optimize prompt strategies for reliability, performance, and cost efficiency.
• Support prompt versioning, experimentation, and controlled rollout strategies.

Backend & API Engineering

• Develop Python-based services and AI backends using FastAPI.
• Expose agent and workflow capabilities via secure, scalable REST APIs.
• Implement asynchronous workflows, background tasks, and event-driven processing where appropriate.

Cloud-Native AI Platform Development

• Build and deploy AI services on AWS, leveraging AWS Bedrock for foundation model access.
• Integrate supporting cloud services (e.g., IAM, logging, monitoring) to meet enterprise security and compliance requirements.
• Optimize solutions for performance, scalability, and cost in a cloud-native environment.

Containerization & Deployment

• Package AI services using Docker and deploy to Kubernetes environments.
• Support deployment pipelines that enable consistent builds across dev, test, and production.
• Collaborate with platform and operations teams to ensure production readiness.

State, Memory & Caching

• Design and implement agent memory and caching strategies using ElastiCache (Redis).
• Optimize retrieval, session state, and intermediate results for performance and reliability.

Observability, Guardrails & Evaluation

• Implement guardrails for safety, compliance, and reliability (input validation, output constraints, tool-use controls).
• Instrument workflows using Langfuse for tracing, evaluation, and observability.
• Build and maintain evaluation harnesses to validate quality, performance, and regression risks prior to releases.
• Support release gates and quality checks for AI workflow deployments.

Release, Versioning & Collaboration

• Contribute to release planning by validating AI workflow readiness and evaluation results.
• Use GitHub for version control, pull requests, code reviews, and documentation.
• Collaborate closely with architects, product owners, and operations teams to support smooth transitions to production.

Continuous Improvement & Learning

• Stay current with emerging agentic AI frameworks, orchestration patterns, and LLM capabilities.
• Identify opportunities to improve reliability, scalability, and developer experience across AI solutions.
• Contribute reusable components, patterns, and best practices to shared repositories.

Required Skills

• Strong Python development experience for AI and backend services.
• Hands-on experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar).
• Experience building APIs using FastAPI.
• Working knowledge of AWS cloud services, including AWS Bedrock.
• Experience with Docker and Kubernetes for containerized deployments.
• Familiarity with Redis / ElastiCache for caching or state management.
• Experience with prompt engineering, prompt testing, and optimization.
• Exposure to guardrails, observability, and evaluation for LLM-based systems.
• Proficiency with GitHub workflows and collaborative development practices.

Preferred Skills

• Experience implementing MCP tools or custom tool abstractions for agents.
• Hands-on use of Langfuse or similar AI observability platforms.
• Experience designing evaluation harnesses for LLM regression testing and release validation.
• Familiarity with enterprise AI governance, security, or compliance requirements.
• AWS certifications (Developer, Solutions Architect, or AI/ML specialty).