AI Developer

Senior AI Developer

You will work across multiple domainsincluding HR, finance, healthcare, and recruitmentdeveloping autonomous agents powered by LLMs, integrated tightly with enterprise systems.

As a senior team member, you will also mentor junior developers and contribute to capability building within the AI practice.

Key Responsibilities :

Build and deploy agentic applications using LLMs and agentic frameworks.

• Design and develop

  MCP-compatible servers

  and integrate agents with enterprise systems (ERP, CRM, HCM).
• Fine-tune LLMs and optimize agent workflows for reliability, cost efficiency, and performance.
• Architect and implement

  agent evaluation pipelines

  , scoring functions, and test harnesses.
• Conduct

  functional, regression, and adversarial testing

  for agent workflows.
• Implement observability dashboards, monitoring, and feedback loops for agents.
• Collaborate closely with product managers, data scientists, and business stakeholders.
• Participate in demos, solution walkthroughs, and stakeholder engagements.
• Mentor junior developers and contribute to

  internal AI capability building

  , best practices, and code reviews.

Mandatory Skills

• Strong proficiency in

  Python

  for backend development and AI engineering.
• Experience with

  agentic frameworks

  such as LangGraph, AutoGen, CrewAI, or similar.
• Hands-on experience with

  cloud platforms

  and GenAI services:

AWS (Bedrock, Lambda, Cognito),

Azure, or

GCP.

• Proven experience building

  AI/ML systems

  , including GenAI agents, RAG pipelines, vector search, and LLM orchestration.
• Expertise with AI services such as

  AWS Bedrock, SageMaker, Inferentia

  ,

  Azure OpenAI

  , or

  Google Vertex AI

  .
• Strong understanding of

  Docker, Kubernetes

  , and CI/CD pipelines for scalable deployment.
• Knowledge of

  GDPR, CCPA

  , and responsible AI principles (fairness, bias mitigation, explainability).
• Experience integrating AI systems with

  MCP servers

  , REST APIs, and enterprise data sources.
• Data engineering experience, including:

• Vector databases

  ,

• Graph databases

  ,

• Ingestion pipelines

  .
• Experience in

  agent evaluation

  , scoring functions, and advanced debugging tools.