Nlp Software Engineer

Job Posting: NLP/Generative AI Engineer

Excited About the Role? Heres What Your Day Might Look Like:

We are on the lookout for a motivated and enthusiastic NLP practitioner to join our dynamic team! If you have a passion for solving complex problems and driving innovative AI solutions, this role is for you. You will work on cutting-edge internal product development and support client-focused projects, with a special focus on the pharma domain.

Your Responsibilities:

• Design, develop, and deploy GenAI applications, addressing challenges like hallucinations, bias, and latency.
• Collaborate with internal teams and external stakeholders to tailor AI solutions to business needs, especially in the pharma industry.
• Contribute to the entire AI lifecycle: ideation, model fine-tuning, deployment, and performance monitoring.
• Apply advanced fine-tuning techniques like LoRA and PEFT to optimize LLMs.
• Develop scalable NLP pipelines for tasks such as text processing, entity recognition, and conversational AI.
• Optimize inference pipelines with techniques like quantization, model distillation, and RAG (Retrieval-Augmented Generation).
• Implement robust monitoring and guardrails to ensure LLM reliability, safety, and compliance.
• Lead MLOps initiatives with CI/CD, Docker, Kubernetes, and cloud deployments (AWS, GCP, Azure).
• Mentor junior engineers and promote best practices in NLP/AI development.

What We're Looking For:

• 3+ years of experience in NLP, AI/ML, or data science with a strong portfolio of production-grade solutions.
• MLOPS , deployment.
• Proficiency in transformer architectures like BERT, GPT, T5, LLaMA, and Mistral.
• Deep understanding of NLP techniques: text preprocessing, tokenization, embeddings, and NER.
• Experience with RAG, vector databases (FAISS, Pinecone, Chroma), and prompt engineering.
• Solid grasp of Python and backend frameworks (Django/Flask) for API and microservices development.
• Familiarity with Agentic AI systems, LLM observability tools, and AI safety guardrails.
• Hands-on experience with MLOps, including CI/CD pipelines and scalable cloud deployments.
• Prior exposure to life sciences, pharma, or other regulated domains is a plus.
• Problem-solving mindset, with the ability to work independently, innovate, and mentor team members.