Atyeti - Lead/Senior Python Developer - Artificial Intelligence/Machine Learning

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 :