MLE-NLP / Data Science / Gen AI -Immediate joiner prefer

Key Responsibilities

• Contribute to solving business problems with cutting-edge AI solutions and innovate on ways to improve cost-efficiency and reliability of those solutions
• Collaborate with Lab lead, product designers, researchers, and architects to scope MVPs and define core capabilities.
• Build modular, reusable components for GenAI solutions or traditional ML model training, evaluation, and deployment
• Design and develop AI tools that can be called via API or through batch transformations for large datasets.
• Implement end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, and performance testing.
• Develop production-grade solutions following software engineering best practices
• Support experimentation, A/B testing, and fine-tuning cycles with the team
• Research and stay up-to-date on the latest advancements in generative AI technologies and methodologies.
• Document architectures, processes, and technical decisions.
• Please note that while you do not need to be an expert in every area, being familiar with most of the following is important. We are looking for someone who can effectively integrate everything, with team support to fill any gaps.
• Bachelors or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of experience in a similar AI/ML engineer role.
• Strong knowledge of Python, SQL, and additional data processing languages.
• Proficiency in basic machine learning concepts and methods, especially those related to GenAI and NLP.
• Experience using open-source LLMs.
• Proficiency in software engineering fundamentals (unit testing, modular code development, CI/CD, etc.)
• Proficiency in core ML/AI frameworks (e.g. scikit-learn, PyTorch, TensorFlow, HuggingFace)
• Familiarity with cloud-based tools (AWS and Azure), containerization (Docker), and version control (Git)
• Experience with ETL tools (e.g., Apache Spark, Airflow), Pyspark, data modeling/warehousing and CI/CD tools is a plus.
• Experience with vector embeddings, multimodal data, or agentic workflows
• Knowledge of LLM fine-tuning, RAG pipelines, and/or hybrid retrieval techniques
• Exposure to healthcare or life science datasets (EHR, claims, clinical trials, etc.)
• Experience working in startup or lab-like R&D environments
• Strong communication skills and comfort working in cross-functional