AI/ML Engineer

DataZymes Analytics is a cutting-edge and innovative data analytics company working with Life Sciences clients that empowers businesses and organizations to unlock the true potential of their data. Our mission is to harness the power of data and transform it into actionable insights, enabling our clients to make informed decisions and gain a competitive edge in today's data-driven world.

With a team of highly skilled data scientists, analysts, and engineers, DataZymes Analytics is committed to delivering top-notch solutions tailored to the unique needs of each client. Aided by our suite of cutting-edge products and accelerators, we are helping our Pharma clients on their Data to Insights journey. In a short span, we have built a high-performance team in focused practice areas, built digital-enabled solutions, and are working with some marquee names in the US healthcare industry.

What sets us apart is our dedication to accuracy, efficiency, and innovation. We utilize the latest technologies and industry best practices to ensure the highest quality of work and the fastest turnaround times. Moreover, our agile approach allows us to adapt quickly to changing requirements and deliver exceptional results within the most demanding timelines.

1. 3+ years of work experience in Python programming for AI/ML, deep learning, and Generative AI model development
2. Proficiency in TensorFlow/PyTorch, Hugging Face Transformers and Langchain libraries
3. Hands-on experience with NLP, LLM prompt design and fine-tuning, embeddings, vector databases and agentic frameworks
4. Strong understanding of ML algorithms, probability and optimization techniques
5. 6+ years of experience in deploying models with Docker, Kubernetes, and cloud services (AWS Bedrock, SageMaker, GCP Vertex AI) through APIs, and using MLOps and CI/CD pipelines
6. Familiarity with retrieval-augmented generation (RAG), cache-augmented generation (CAG), retrieval-integrated generation (RIG), low-rank adaptation (LoRA) fine-tuning
7. Ability to write scalable, production-ready ML code and optimized model inference 
8. Experience with developing ML pipelines for text classification, summarization and chat agents
9. Prior experience with SQL and noSQL databases, and Snowflake/Databricks