Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to design, build, and deploy agentic AI systems powered by cutting-edge Generative AI (GenAI) technologies. You will work at the intersection of LLMs, autonomous agents, and real-world enterprise workflows, creating intelligent systems that can reason, plan, and execute complex tasks. This role requires deep expertise in machine learning, LLM orchestration, and scalable system design, along with a strong product mindset.
Responsibilities:
Design and develop agentic AI systems capable of multi-step reasoning, planning, and tool usage.
Build and optimize applications using Large Language Models (LLMs) (e. g., GPT, open-source models).
Develop multi-agent architectures, workflows, and orchestration pipelines.
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.
Fine-tune, evaluate, and monitor LLM performance in production environments.
Integrate AI agents with APIs, databases, and enterprise systems.
Ensure scalability, reliability, and low-latency deployment of ML systems.
Collaborate with product, backend, and frontend teams to deliver AI-driven features.
Stay up to date with the latest advancements in GenAI, autonomous agents, and LLM frameworks.
Autonomous AI agents that can plan, reason, and execute tasks.
AI copilots for enterprise workflows (support, finance, operations, etc. ).
Scalable GenAI platforms powering real-time applications.
End-to-end AI systems from prototype production.
Requirements:
5+ years of experience in Machine Learning / AI Engineering.
Strong hands-on experience with Python and ML frameworks like PyTorch / TensorFlow.
Solid understanding of LLMs, transformers, prompt engineering, and fine-tuning techniques.
Experience with agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, etc. ).
Experience in building RAG pipelines using vector databases (FAISS, Pinecone, Weaviate, etc. ).
Strong backend engineering skills (APIs, microservices, system design).
Familiarity with cloud platforms (AWS / GCP / Azure) and containerization (Docker, Kubernetes).
Experience in model evaluation, observability, and monitoring.
Strong problem-solving skills and ability to work in fast-paced environments.
Good to Have:
Experience with multi-modal models (vision + text).
Knowledge of reinforcement learning / RLHF.
Exposure to AI safety, guardrails, and alignment techniques.
Experience deploying LLMs using vLLM, TensorRT-LLM, or similar inference engines.
Contributions to open-source AI/ML projects.

Experience
4-8 yrs