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.