Data & Ai Practice Head

Key Responsibilities

Strategic Leadership

• Define and execute the Data & AI practice strategy across data engineering, analytics, AI/ML, GenAI, MLOps, and data governance.
• Track industry trends, emerging technologies, and competitive landscapes to strengthen Focaloid’s positioning.
• Expand the service portfolio with new Data & AI offerings aligned to evolving client needs and market opportunities.

Practice & Capability Building

• Build, mentor, and scale a multidisciplinary team of data engineers, AI/ML experts, data scientists, and analysts.
• Establish competency development frameworks and drive continuous upskilling in modern data stacks, cloud platforms, and AI technologies.
• Oversee workforce planning, hiring, and resource allocation to meet delivery and growth objectives.

Client Engagement & Solutioning

• Partner with sales and presales teams to craft compelling Data & AI solutions, proposals, and presentations.
• Engage with C‑suite stakeholders to understand business challenges and design tailored, outcome‑driven solutions.
• Build long‑term client relationships and expand existing accounts through consultative engagement.

Delivery Excellence

• Provide architectural oversight for large‑scale data platforms, AI/ML solutions, and cloud‑native data ecosystems.
• Ensure timely, high‑quality delivery of projects with strong alignment to client expectations and business outcomes.
• Establish and enforce best practices for data quality, security, governance, and compliance (GDPR, HIPAA, etc.).

Innovation & Thought Leadership

• Drive R&D initiatives in emerging Data & AI technologies, including GenAI, LLMOps, real‑time analytics, and automation.
• Pilot innovative solutions and accelerators to strengthen Focaloid’s competitive edge.
• Represent Focaloid in conferences, industry forums, and thought leadership platforms through whitepapers, talks, and expert contributions.

Business Growth

• Identify new business opportunities and drive revenue growth across Data & AI service lines.
• Build strategic partnerships with cloud providers, AI platform vendors, and technology partners.
• Collaborate with marketing to strengthen employer branding and market visibility for the Data & AI practice.

Technical Skills & Experience

Conventional Data & Machine Learning

• Data engineering: SQL, ETL, data pipelines, data warehousing
• Feature engineering, model development, hyperparameter optimization
• Programming: Python, R, SQL, PySpark, Scala
• ML frameworks: TensorFlow, PyTorch, Scikit‑learn, XGBoost, LightGBM
• Applied ML: time‑series forecasting, document classification, clustering, anomaly detection

GenAI & Large Language Models

• Retrieval‑Augmented Generation (RAG) architectures
• Vector databases: Qdrant, Pinecone, FAISS, Chroma
• Multi‑agent frameworks: LangGraph, CrewAI, AutoGen, Haystack Agents

• Prompt engineering, fine‑tuning, LoRA, embeddings
• Document intelligence: OCR, structured/unstructured extraction
• Tool use, function calling, and agentic workflows

Cloud & Data Platforms

• Azure: Azure AI, Azure OpenAI, Data Factory, Synapse, Databricks
• AWS: Bedrock, S3, EC2, RDS, Glue, Athena, SageMaker
• GCP: Vertex AI, BigQuery, Dataflow, Pub/Sub
• Snowflake, Redshift, PostgreSQL, Kafka

MLOps & Productionization

• CI/CD for ML and GenAI pipelines
• Model deployment: Docker, Kubernetes
• Monitoring: drift detection, accuracy tracking, hallucination checks
• API orchestration, microservices, event‑driven architecture

Requirements

• Bachelor’s degree in Engineering or related field with 15+ years of experience delivering enterprise data and AI solutions using Agile methodologies.
• Proven experience as a Practice Head, Delivery Head, or Data Transformation Leader building Data & AI capabilities.
• Strong background in leading large‑scale data engineering, analytics, AI/ML, and cloud transformation programs.
• Experience working in a matrix environment with global sales, delivery, and marketing teams.
• Demonstrated success in presales, proposal creation, solutioning, and business development.
• Deep expertise in modern data technologies, cloud platforms (AWS/Azure/GCP), AI/ML frameworks, and GenAI ecosystems.

• Excellent leadership skills with the ability to influence, inspire, and drive high‑performance teams.
• Strong communication and presentation skills, including experience engaging with C‑suite stakeholders.
• Resilient, resourceful, and comfortable working in fast‑paced, multicultural, multi‑location environments.
• Proven ability to build trusted relationships with clients and internal stakeholders.