Senior/Lead NLP Engineer

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops

• Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation.
• Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models.
• Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI.
• Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness.
• Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks.
• Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments.
• Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency.
• Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure).
• Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture.
• Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences.

Key Qualifications:

• 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions.
• Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques.
• Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER).
• Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering.
• Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails.
• Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise.
• Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies.
• Prior experience in life sciences, pharma, or other regulated industries is a plus.
• A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.