Curious about the role? What your typical day would look like?
· We’re seeking a motivated and enthusiastic NLP practitioner to contribute to solving complex problems for our clients. In this role, you will work on internal product development and support client-focused projects, with exposure to the pharma domain being a plus. You'll collaborate with experienced team members and learn to create impactful AI solutions while optimizing and developing GenAI applications. Your responsibilities will include:
· Supporting the design, development, and deployment of GenAI solutions, learning to address challenges like hallucinations, bias, and latency, while contributing to performance and reliability improvements.
· Collaborating with both internal teams and external stakeholders, particularly in the pharma space, to understand business requirements and contribute to the development of tailored AI-powered systems.
· Assisting in the full lifecycle of AI project delivery, including ideation, model fine-tuning, deployment, and performance monitoring under the guidance of senior team members.
· Learning and applying fine-tuning techniques (such as LoRA, PEFT) to Large Language Models (LLMs) for specific business needs.
· Assisting in the development of scalable pipelines for AI model deployment, including handling error management, monitoring, and retraining strategies.
· Actively participating in a collaborative environment, sharing ideas and working as part of a dynamic team of data scientists and AI engineers.
Job Requirement
Who do we expect?
Key Responsibilities:
- 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.