Model Development:
- Design, build, and deploy production-grade AI/ML solutions with a focus on scalable, real-world applications.
Programming & Data Handling:
- Develop and implement machine learning models using Python, PySpark, SQL, and popular ML libraries such as Scikit-learn, XGBoost, and LightGBM.
Cloud ML Platforms:
- Build and manage cloud-native ML solutions using platforms like AWS SageMaker, GCP Vertex AI, or Azure ML.
NLP & GenAI Expertise:
- Apply strong knowledge in natural language processing and generative AI frameworks including Hugging Face, LangChain, and LlamaIndex.
MLOps & Deployment:
- Utilize MLOps tools such as MLflow, Weights & Biases, and DVC for model tracking and versioning. Deploy models using FastAPI, Flask, Docker, and Kubernetes.
Vector Databases & Advanced Use Cases:
- Work with vector databases to enable semantic search and other advanced GenAI use cases.
Text-to-SQL Integration:
- Apply familiarity with SQL Coder or Text-to-SQL models to integrate natural language interfaces with structured data sources.
Stakeholder Collaboration:
- Communicate effectively with stakeholders, translate business problems into technical solutions, and collaborate closely with cross-functional teams.
Education and Work Experience:
- B.E / B.Tech or equivalent degree in Computer Science, Information Technology, or a related field
- 3–5 years of hands-on experience in developing and deploying AI/ML solutions in production environments
Specialized Knowledge, Skills, and Abilities:
- Proficiency in Python, PySpark, and SQL
- Experience with Scikit-learn, XGBoost, LightGBM
- Strong hands-on experience with AWS SageMaker, GCP Vertex AI, or Azure ML
- Deep understanding of NLP/GenAI technologies: Hugging Face, LangChain, LlamaIndex
- Working knowledge of MLOps tools: MLflow, Weights & Biases, DVC
- Familiarity with deployment using FastAPI, Flask, Docker, Kubernetes
- Experience with Vector Databases and Text-to-SQL models
- Excellent communication, stakeholder engagement, and team collaboration skills
