Lead/ Senior Data Scientist_ Exp: 8+years

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.