Senior AI/ML Engineer- GenAI & Machine Learning

Experience: 5+ Years

Location: Remote – India About the Role We are looking for an experienced Senior AI/ML Engineer with 5+ years of hands-on experience in Machine Learning, Generative AI, LLMs, and production-grade AI systems. The ideal candidate should have strong expertise in Python, ML/DL, LLM application development, RAG, AI agents, model deployment, and MLOps. You will be responsible for designing, developing, deploying, and optimizing scalable AI solutions that solve real-world business problems.

Key Responsibilities - Design, develop, and deploy production-grade Machine Learning and Generative AI solutions. - Build and optimize LLM-powered applications, including RAG systems, AI assistants, and intelligent automation workflows. - Develop Agentic AI / AI agent systems with tool calling, workflow orchestration, memory, and multi-step reasoning. - Build RAG pipelines involving document processing, chunking, embeddings, vector search, retrieval, reranking, and response generation. - Work with LLMs from providers such as OpenAI, Anthropic, Google, Meta, or open-source/Hugging Face models. - Develop and fine-tune ML/DL models using frameworks such as PyTorch, TensorFlow, or Scikit-learn. - Design and implement ML pipelines, model serving, inference APIs, and scalable AI services. - Implement model and LLM evaluation frameworks covering accuracy, relevance, hallucination, latency, cost, and safety. - Build AI services and APIs using Python, FastAPI/Flask, and modern backend architectures. - Deploy and operate AI/ML workloads using Docker, Kubernetes, CI/CD, and cloud platforms. - Implement MLOps practices including model versioning, experiment tracking, monitoring, retraining, and production observability. - Optimize model performance, inference latency, scalability, and cloud/GPU costs. - Collaborate with product, engineering, data, and business teams to convert requirements into AI solutions. - Mentor junior engineers and contribute to technical architecture and engineering best practices.

Required Skills Core AI/ML - 5+ years of professional experience in AI/ML, Machine Learning, Data Science, or related engineering roles. - Strong proficiency in Python. - Strong understanding of Machine Learning algorithms, statistics, feature engineering, model evaluation, and experimentation. - Hands-on experience with Deep Learning using PyTorch and/or TensorFlow. - Strong understanding of NLP and modern language-model architectures.

Generative AI / LLM - Strong hands-on experience with LLMs and Generative AI applications. - Experience building RAG-based applications. - Strong understanding of: - Embeddings - Vector databases - Semantic search - Hybrid search - Reranking - Prompt engineering - Context management - LLM evaluation - Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks. - Experience building AI agents, tool calling, multi-step workflows, or multi-agent systems is highly desirable. - Experience with Hugging Face and/or open-source LLMs is a plus. - Experience with LLM fine-tuning, LoRA/QLoRA, PEFT, or model adaptation is a plus.

MLOps & Cloud - Hands-on experience deploying ML/AI applications to production. - Experience with Docker and Kubernetes. - Experience with MLflow, Kubeflow, or similar MLOps platforms. - Experience with CI/CD pipelines and production monitoring. - Experience with at least one major cloud platform: AWS, Azure, or GCP. - Understanding of GPU-based inference and model optimization is a plus.

Data & Engineering - Strong SQL and experience working with structured/unstructured data. - Experience with REST APIs and microservices. - Experience with databases such as PostgreSQL, MongoDB, or similar. - Familiarity with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or FAISS. - Strong Git and software engineering practices.

Good to Have - Experience with AI evaluation and observability tools such as LangSmith, Ragas, TruLens, or equivalent. - Experience with multimodal AI involving text, image, audio, or video. - Experience with model quantization and inference optimization. - Knowledge of AI safety, guardrails, responsible AI, and data privacy. - Experience building AI products for SaaS, FinTech, Healthcare, or Enterprise applications. - Open-source contributions, research publications, or strong GitHub projects.