Senior Data & AI Engineer

Responsibilities

We are looking for a highly skilled Data & AI Engineer with 5–6 years of experience in building scalable data platforms and AI‑driven solutions. This role requires hands‑on expertise in modern data engineering, LLM‑based systems, agentic AI, RAG pipelines, and MCP/LangGraph‑based orchestration.

The ideal candidate is someone who can design end‑to‑end data and AI systems—from ingestion to analytics to intelligent automation—while working closely with business, engineering, and product teams.

AI & Agentic Automation

• Build agentic AI workflows using MCP, LangChain, LangGraph, and multi‑agent orchestration frameworks
• Develop RAG pipelines using vector databases, embeddings, chunking strategies, and retrieval optimization
• Implement LLM‑powered automation for ingestion, classification, summarization, and decision support
• Deploy and monitor LLM applications using Azure OpenAI, AWS Bedrock, or GCP Vertex AI

Data Engineering & Architecture

• Design and implement ETL/ELT pipelines for structured, semi‑structured, and unstructured data
• Build scalable data platforms using Azure Data Factory, AWS Glue, Databricks, or Fabric Data Pipelines
• Develop and optimize data lakehouse architectures (Delta Lake, Iceberg, or Fabric Lakehouse)
• Implement data warehouse solutions using Snowflake, Azure Synapse, or BigQuery

Analytics & BI

• Build dashboards and semantic models using Power BI / Fabric
• Implement data quality, lineage, and governance frameworks
• Work with business teams to translate requirements into analytical models

ML Engineering

• Develop and deploy ML models for forecasting, classification, clustering, and anomaly detection
• Implement MLOps/LLMOps pipelines for model deployment, monitoring, and retraining
• Work with NLP, embeddings, and vector search technologies

Cloud & DevOps

• Work with Azure / AWS / GCP for compute, storage, networking, and security
• Implement CI/CD pipelines for data and AI workloads
• Containerize workloads using Docker; Kubernetes experience is a plus

Requirements

Technical Skills Required

Programming

• Python (Pandas, NumPy, Scikit‑learn, FastAPI, LangChain/LangGraph)
• SQL (advanced queries, performance tuning, stored procedures)
• Experience with REST APIs, webhooks, and integration patterns

AI/ML & LLM Engineering

• Strong understanding of LLMs, embeddings, vector stores (FAISS, Pinecone, Chroma, Redis)
• Experience with RAG, prompt engineering, and LLM fine‑tuning
• Experience with agentic AI frameworks (MCP, LangGraph, LangChain Agents)
• Knowledge of NLP, document intelligence, or computer vision is a plus

Data Engineering

• Data lakes, lakehouses, and warehouse architectures
• ETL/ELT tools (ADF, Glue, Databricks, Fabric Pipelines)
• Streaming technologies (Kafka, EventHub) — optional

Cloud & DevOps

• Azure / AWS / GCP
• CI/CD (GitHub Actions, Azure DevOps, GitLab)
• Docker; Kubernetes (optional)

Soft Skills

• Strong analytical and problem‑solving mindset
• Ability to work with cross‑functional teams (business, product, engineering)
• Excellent communication and documentation skills
• Experience working in Agile/Scrum environments

Education

• Bachelor's or master's degree in computer science, Data Science, IT, or related field