*Consultants local to INDIA are eligible.

1. Structured Data (80–90%) – Predictive analytics, forecasting, cost estimation, likelihood modeling, and batch‑oriented machine learning pipelines.
2. Text / Unstructured Data (NLP & GenAI) – Building low‑latency real‑time systems using deep learning, LLMs, prompt engineering, and agentic AI frameworks.

• Build, deploy, and optimize ML models for predictive analytics, forecasting, classification, and regression.
• Perform large-scale feature engineering using PySpark and Big Data tools.
• Work on batch pipelines, model versioning, and experiment tracking.
• Develop cost estimation and risk/likelihood models using statistical and ML techniques.

• Build NLP pipelines using deep learning frameworks such as PyTorch, TensorFlow, or similar.
• Develop real‑time, low‑latency inference systems for text classification, embeddings, semantic search, summarization, and retrieval.
• Create prompts, context graphs, and agentic workflows for LLM-based systems.
• Apply knowledge of prompt engineering, context engineering, and autonomous agent frameworks to production systems.

• Work in Databricks for ETL, feature engineering, ML training, and orchestration.
• Use Azure services for model deployment, data pipelines, and infrastructure.
• Collaborate using Git-based workflows; leverage tools like GitHub Copilot, Claude Code, etc.
• Implement model monitoring, observability, drift detection, and performance tracking.

• Strong hands-on experience with Databricks (Delta Lake, MLflow, Job Orchestration).
• Excellent PySpark skills for large-scale distributed data processing.
• Proficiency in Azure cloud services (ADF, Azure ML, AKS, Databricks on Azure).
• Strong understanding of ML algorithms, statistical methods, and data analysis.
• Experience with deep learning frameworks:
  • PyTorch
  • TensorFlow
  • Transformers (HuggingFace)
• Experience with model monitoring and ML observability.
• Ability to write clean, optimized code and leverage AI code assistants.

• Prompt engineering (task prompts, chain of thought, tool calling, retrieval prompts).
• Context engineering (retrieval pipelines, RAG, memory management, context structuring).
• Knowledge of LLM-based agentic frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.).
• Experience with vector databases and embedding models is a plus.

• Experience with containerization (Docker, Kubernetes, AKS).
• Experience deploying models to production (REST APIs, real-time endpoints).
• Knowledge of streaming technologies (Kafka, EventHub, Spark Streaming).
• Understanding of CI/CD for ML (Azure DevOps / GitHub Actions).

• A problem solver who is comfortable working with both structured and unstructured data.
• Someone who enjoys using modern AI tools to accelerate development.
• A data scientist who writes clean, production-grade code.
• A collaborator who thrives in cross-functional teams and fast-paced environments.

Flexible work from home options available.