- Company Name
- Edify Technologies
- Job Title
- Full-Stack AI/ML Engineer
- Job Description
-
Job title: Full‑Stack AI/ML Engineer
Role Summary: Design, develop, and deploy AI‑driven solutions that transform data into production‑ready models and services. Lead end‑to‑end AI pipelines—from data acquisition and feature engineering to LLM integration, model deployment via microservices, and continuous optimization—while collaborating with product owners and cross‑functional Agile teams.
Expectations:
- 5+ years of hands‑on experience in data science, ML engineering, or applied AI with scalable production deployments.
- Proven ability to take prototypes from concept to fully operational models and iterate them for performance and reliability.
- Strong communication, documentation, and teamwork skills in Agile/Scrum environments.
Key Responsibilities:
- Acquire and curate data, build ETL pipelines, and engineer features using APIs, cloud storage, or relational databases.
- Develop ML models with scikit‑learn, TensorFlow, PyTorch, or similar frameworks; apply rigorous EDA, validation, and experiment tracking.
- Implement LLM and generative AI solutions with LangChain/LangGraph and vector databases for semantic retrieval/RAG pipelines.
- Build and expose models as FastAPI microservices; package, containerize, and orchestrate using Docker and Kubernetes.
- Design and maintain MLOps pipelines; configure CI/CD workflows and monitor model performance in production.
- Deploy and manage solutions on Azure, AWS, or GCP, utilizing their AI/ML services and best practices.
- Participate actively in sprint planning, daily stand‑ups, sprint reviews, and retrospectives; collaborate with product owners and program managers.
Required Skills:
- Python (Pandas, NumPy, scikit‑learn, TensorFlow/PyTorch, LangChain, LangGraph)
- SQL and relational database concepts
- FastAPI for API services
- Docker, Kubernetes, CI/CD tooling
- Cloud platforms: Azure, AWS, or GCP with ML/AI offerings
- LLMs, generative AI frameworks, vector databases
- EDA, model validation, experiment tracking tools
- Agile/Scrum work style
- Excellent written and verbal communication
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field (or equivalent professional experience)
- Optional: Cloud or ML certifications (e.g., AWS Certified ML – Specialty, Azure AI Engineer Associate, GCP Professional Data Engineer) may be advantageous.