Lead​/Senior Python Developer + Data Scientist_ Exp

Position: 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, Lang Chain, Lang Graph, 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 (Cloud Formation/Terraform).
Champion Dev Ops 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, Lang Chain, Lang Graph, 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.