Lead / Senior Python Developer + 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.