Senior Analyst at eClerx Career Site

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

29 Jun, 26

Salary

0.0

Posted On

31 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Agentic AI, LLMs, Python, LangChain, LlamaIndex, AutoGen, CrewAI, API Integration, Reinforcement Learning, RAG, Prompt Engineering, Vector Databases, Docker, Kubernetes, Software Engineering, Agent Orchestration

Industry

IT Services and IT Consulting

Description

eClerx is seeking an innovative Agentic AI Developer to design, build, and deploy autonomous AI agents capable of reasoning, decision-making, and task automation. The role involves developing multi-agent systems using LLMs, integrating APIs and tools, and optimizing performance through reinforcement learning and memory-driven architectures. The ideal candidate combines strong software engineering skills with deep knowledge of AI frameworks, prompt engineering, and agent orchestration platforms. Work Experience * Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field. * 5+ years of experience in AI/ML or software development with hands-on exposure to LLMs. * Strong proficiency in Python and experience with AI frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI. * Experience working with LLM APIs (OpenAI, Anthropic, Google Gemini, etc.) and vector databases (Pinecone, Chroma, FAISS). * Knowledge of agent orchestration, prompt engineering, and context memory management. * Familiarity with cloud environments (AWS, GCP, Azure) and containerization (Docker, Kubernetes). Responsibilities Design and Develop AI Agents: Build intelligent, autonomous agents leveraging LLMs, memory frameworks, and multi-agent coordination systems (e.g., LangChain, CrewAI, AutoGen). Integrate APIs and Tools: Connect agents with third-party APIs, knowledge bases, and internal systems to enable dynamic data access and real-time task execution. Optimize Agent Performance: Apply reinforcement learning, context management, and retrieval-augmented generation (RAG) techniques to enhance reasoning and accuracy. Develop End-to-End Pipelines: Create scalable, production-ready pipelines for AI agent deployment, monitoring, and continuous improvement. Collaborate with Cross-Functional Teams: Partner with product managers, ML engineers, and domain experts to translate business use cases into functional agentic systems

Responsibilities

The role involves designing, building, and deploying autonomous AI agents leveraging LLMs, memory frameworks, and multi-agent coordination systems. Responsibilities also include integrating these agents with external APIs and optimizing their performance using techniques like reinforcement learning and RAG.