AI Engineer (Agentic AI & LLM Systems) - Contract | Immediate

AI Engineer (Agentic AI & LLM Systems) with AI Start Up Contract| Immediate

Your tasks AI Start Up is looking for an experienced AI Engineer to join their growing AI team. You’ll play a key role in developing intelligent, agentic AI systems using cutting-edge large language models (LLMs), multi-agent orchestration, and retrieval-augmented generation (RAG). This is a hands-on role combining software engineering and a passion for building next-gen autonomous agents.

You’ll collaborate closely with AI leads, backend engineers, data engineers, and product managers to bring scalable and intelligent systems to life—integrated into real-world procurement and business applications.

Key Responsibilities · Design and implement agentic AI pipelines using LangGraph, LangChain, CrewAI, or custom frameworks · Build robust retrieval-augmented generation (RAG) systems with vector databases (e.g., OpenSearch) · Fine-tune, evaluate, and deploy LLMs for task-specific applications · Integrate external tools and APIs into multi-agent workflows using dynamic tool/function calling (e.g., OpenAI JSON schema) · Develop memory modules such as short-term context, episodic memory, and long-term vector stores · Build scalable, cloud-native services using

Python · Collaborate in agile, cross-functional teams to rapidly prototype and ship Multiagent-based features · Monitor and evaluate agent performance using tailored metrics (e.g., success rate, hallucination rate) · Ensure secure, reliable, and maintainable deployment of AI systems in production environments

Desired Persona · 7+ years of professional experience in machine learning, NLP, or software engineering · Strong proficiency in Python and experience with ML libraries like PyTorch, TensorFlow, scikit-learn, and XGBoost · Hands-on experience with LLMs (e.g., GPT, Claude, LLaMA, Mistral) and NLP tooling such as LangChain, HuggingFace, and Transformers · Experience designing and implementing RAG pipelines with chunking, semantic search, and reranking · Familiarity with agent frameworks and orchestration techniques (e.g., planning, memory, role assignment) · Deep understanding of prompt engineering, embeddings, and LLM architecture basics · Design systems with role-based communication, coordination loops, and hierarchical planning. Optimize agent collaboration strategies for real-world tasks. · Experience integrating REST/GraphQL APIs into ML workflows · Strong collaboration and communication skills, with a builder’s mindset and · willingness to explore new approaches

Good to Have · Solid foundation in microservice architectures, CI/CD, and infrastructure-as-code (e.g., Terraform) · Experience with RLHF, LoRA, or parameter-efficient LLM fine-tuning · Familiarity with CrewAI, AutoGen, Swarm, or other multi-agent libraries · Exposure to cognitive architectures like task trees, state machines, or episodic memory · Prompt debugging and LLM evaluation practices · Awareness of AI security risks (e.g., prompt injection, data exposure)

Screening Criteria · 5+ years of professional experience in machine learning · Experience of implementation of AI pipelines using langchain and langgraph · Experience in RAG systems deployment · Expertise in Strong coding using Python · Understanding of Multi agent frameworks (CrewAI, AutoGen, etc.) · Available to join within 2 weeks

Evaluation Process In the interview process, there will be online coding test, including Python coding (test cases/debugging) and RAG application development

Considerations · 6 Months Contract · Remote Working · Timings – 12 PM to 9 PM IST