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.