Job Summary
20+ years in IT, with at least 15+ years designing and delivering AI/ML solutions at enterprise scale.A proven record of leading multiple AI programs in parallel to production, with cross-functional teams, budgets and measurable business outcomes.Hands-on depth across classical ML, deep learning (NLP, computer vision, time series) and GenAI (LLMs, RAG, fine-tuning, agents).Production experience on at least one major cloud (AWS, Azure or GCP) and its AI/ML services.Experience in client-facing, consulting or solution-architect roles, including shaping large engagements and proposals.Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or a related field. A Master's or PhD is preferred.
Key Responsibilities
Key responsibilitiesAI architecture and strategy: Define the enterprise AI reference architecture and roadmap across ML, deep learning, GenAI, RAG and agentic systems. Make build, buy or partner calls on models, platforms and vendors.Delivery ownership: Lead end-to-end delivery of multiple concurrent AI programs, from use-case discovery and proof of concept to production and scale. Own scope, timelines, quality and risk.Solution design and review: Architect scalable, secure and cost-efficient solutions. Review designs for data pipelines, model serving, latency and inference cost.MLOps and LLMOps: Set standards for model CI/CD, experiment tracking, feature stores, evaluation, monitoring, drift detection and rollback.Responsible AI and governance: Build security, privacy, fairness, explainability and regulatory compliance into every solution. Define guardrails for GenAI use.Stakeholder engagement: Work with CXOs, business owners and clients to turn business problems into AI solutions with measurable ROI. Support solution shaping, pre-sales and executive presentations.Team leadership: Lead and mentor data scientists, ML engineers and solution architects. Run the architecture review board and hire for the team.Innovation and reuse: Track new research and tools, run proofs of concept, and build reusable accelerators and IP.
Skill Requirements
. ML and Deep Learning
- Scikit-learn
- XGBoost
- PyTorch
- TensorFlow
- Natural Language Processing (NLP)
- Computer Vision
- Forecasting
- Recommender Systems
2. GenAI and Agents
- LLM APIs
- GPT
- Claude
- Llama
- Mistral
- Prompt Engineering
- Fine-tuning
- LoRA / PEFT
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- LangChain
- LlamaIndex
- LangGraph
- LLM Evaluation
- Guardrails
3. MLOps and LLMOps
- MLflow
- Kubeflow
- Amazon SageMaker
- Azure ML
- Vertex AI
- Databricks
- Model Registry
- CI/CD
- Model Monitoring
- Drift Detection
4. Data Platforms
- Apache Spark
- Apache Kafka
- Databricks
- Snowflake
- Lakehouse Platforms
- Feature Stores
- Data Governance
5. Cloud and Infrastructure
- AWS
- Microsoft Azure
- Google Cloud Platform (GCP)
- Kubernetes
- Docker
- Terraform
- GPU Optimization
- vLLM
- NVIDIA Triton
- Inference Serving
6. Security and Compliance
- Identity and Access Management (IAM)
- Data Privacy
- Model Risk Management
- Responsible AI
- GDPR
- EU AI Act
7. Programming Languages
Primary:
- Python – Expert
- SQL
Good to have:
- Java
- Scala
- Go