Senior Analyst at eClerx Career Site

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

02 Aug, 26

Salary

0.0

Posted On

04 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Large Language Models, LangChain, LlamaIndex, AutoGen, CrewAI, Vector Databases, Prompt Engineering, RAG, Reinforcement Learning, AWS, GCP, Azure, Docker, Kubernetes, Agentic AI

Industry

IT Services and IT Consulting

Description

Job Title: Agentic AI Engineer / Agentic AI Developer Experience: 5–9 Years Location: Mumbai / Pune Employment Type: Full-Time About the Role eClerx is seeking an innovative and highly skilled Agentic AI Engineer to design, develop, and deploy autonomous AI agents capable of reasoning, decision-making, and task automation. This role focuses on building scalable, production-grade agentic systems using Large Language Models (LLMs), multi-agent orchestration frameworks, and memory-driven architectures. The ideal candidate combines strong software engineering fundamentals with deep expertise in modern AI frameworks and agent-based system design. -------------------------------------------------------------------------------- Key Responsibilities 1. Design & Development of AI Agents * Design, build, and deploy intelligent autonomous AI agents leveraging LLMs, memory frameworks, and multi-agent coordination systems. * Develop agentic workflows using frameworks such as LangChain, CrewAI, AutoGen, and LlamaIndex. * Implement reasoning, planning, and decision-making capabilities within agent systems. 2. Multi-Agent Systems & Orchestration * Design and manage multi-agent architectures enabling collaboration, task delegation, and role-based behaviors. * Optimize agent communication, tool usage, and execution flow for complex tasks. 3. API & Tool Integration * Integrate AI agents with third-party APIs, internal systems, databases, and external tools. * Enable real-time data access, tool calling, and dynamic task execution. 4. Performance Optimization & Intelligence Enhancement * Apply Reinforcement Learning (RL) techniques to improve agent decision-making and efficiency. * Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases. * Optimize prompt engineering, context management, and memory strategies for accuracy and scalability. 5. End-to-End AI Pipelines * Build scalable, production-ready pipelines for agent deployment, monitoring, logging, and continuous improvement. * Ensure reliability, observability, and performance in real-world environments. 6. Collaboration & Stakeholder Engagement * Work closely with product managers, ML engineers, and domain experts to translate business requirements into functional agentic AI solutions. * Participate in design reviews, architecture discussions, and solution planning. -------------------------------------------------------------------------------- Required Qualifications Education * Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Experience * 5+ years of experience in software development and/or AI/ML engineering. * Hands-on experience working with Large Language Models (LLMs) in production environments. Technical Skills * Strong proficiency in Python. * Experience with agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI. * Hands-on experience with LLM APIs (OpenAI, Anthropic, Google Gemini, or similar). * Experience with vector databases like Pinecone, Chroma, FAISS. * Solid understanding of prompt engineering, agent orchestration, and context/memory management. * Familiarity with RAG architectures and knowledge retrieval techniques. * Experience working in cloud environments (AWS, GCP, Azure). * Knowledge of containerization and orchestration tools such as Docker and Kubernetes. -------------------------------------------------------------------------------- Preferred / Good-to-Have Skills * Experience with reinforcement learning for agent optimization. * Exposure to monitoring and evaluation of LLM-based systems. * Understanding of security, scalability, and cost optimization for AI systems. * Experience deploying AI solutions in enterprise or client-facing environments.

Responsibilities

The role involves designing, developing, and deploying autonomous AI agents using LLMs and multi-agent orchestration frameworks. You will also be responsible for integrating these agents with external tools and optimizing their performance through RAG and reinforcement learning.