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Role
Project Lead, Industrial AI & Process Optimization
No. of positions
1
Location
Kolkata
Experience
8-12 Years
Education
B.E./B.Tech or M.Tech in Computer Science, IT or a related field, or MCA.
Role summary
We are hiring a Project Lead to own the technical delivery of AI-driven process optimization solutions for refinery and petrochemical clients. The solutions run on mcube™ , our enterprise AI and data platform. The Project Lead is the senior hands-on engineer on the project. They design the solution architecture, lead a team of 5–10 engineers, and take the system from design through site commissioning to production support.
Key responsibilities
1. Project Management
- Develop the Project Management Plan: WBS, milestones, effort estimates, and resource plan.
- Establish and maintain the delivery baseline (scope, schedule, and cost), track variances, and address deviations.
- Track execution against baseline; identify and address deviations early.
- Update stakeholders on progress at each governance checkpoint.
- Plan and facilitate sprint ceremonies: planning, daily stand-ups, reviews, and retrospectives.
- Define clear, achievable sprint goals and track velocity and burn-down.
- Remove impediments blocking sprint progress.
- Own end-to-end quality of all Project deliverables.
- Enforce QA checkpoints and defect management processes.
- Ensure Quality Gate fulfilment prior to Phase movement.
- Track defect metrics across SDLC phases.
- Ensures that peer-review is conducted and review observations fixed at defined quality-points through the length of the SDLC
- Coordinate UAT and formal sign-off activities with the CPM and BA
2. Technical Leadership
- Contributes to the definition of the solution and deployment architecture across plant OT and IT networks, with sizing, high availability and security.
- Ensures writing of the technical sections of the design specification: data flows, interfaces, APIs and the data model.
- Lead development of data pipelines, model-serving APIs, optimization services and dashboards on mcube.
- Set coding standards; run code reviews, branching strategy and CI/CD pipelines.
- Turn data science models into production services with monitoring and retraining.
- Integrate real-time plant data from DCS through OPC/edge servers, plus laboratory and enterprise systems.
- Own data quality, validation and time-series storage design.
- Install and configure the platform on virtualized, on-premise Kubernetes clusters at client sites.
- Lead SIT, performance testing, security testing (VAPT) fixes, UAT support and site acceptance.
- Hand over to support with runbooks; resolve production incidents within SLA.
Must have
Experience
- B.E./B.Tech or M.Tech in Computer Science, IT or a related field, or MCA.
- 8-12 years in software engineering, with 3-5 years as a Project Lead for data, analytics or AI platforms.
- Delivered at least two production systems end to end: design, build, on-premise or cloud deployment, and support.
- Led teams of 5+ engineers in agile delivery.
Technical skills
- Data ingestion and processing
- Python, PySpark/Apache Spark, batch and streaming pipelines, Kafka or similar message queues, REST and OPC data sources
- Lakehouse and storage
- Delta Lake, object storage (S3/MinIO), PostgreSQL and MariaDB, Elasticsearch,
time-series data modelling
- AI/ML
- scikit-learn, gradient boosting, time-series forecasting, optimization libraries; MLOps for model versioning, monitoring and retraining
- GenAI and agents
- LLM integration, RAG and vector search, agent frameworks (A2A, MCP), prompt and guardrail design
- Services and APIs
- FastAPI microservices, REST API design, authentication and RBAC, Docker
- UI and BI
- Low-code UI and BI dashboards, JavaScript/React basics, role-based report design
- Platform and infrastructure
- Kubernetes, Helm, RHEL/Linux administration, VMware virtualization, Windows Server for edge nodes, load balancing and HA
- DevSecOps
- Git, GitLab, Jenkins CI/CD, container registry, observability (Prometheus/Grafana or similar), vulnerability scanning and VAPT closure
Practices
- Strong Python and SQL; clean, tested code with unit and integration tests.
- Secure design: encryption, secrets management, network segregation, least-privilege access.
- Performance tuning and capacity sizing for data-heavy workloads.
- Clear technical documentation and design reviews.
- Jira and Confluence for backlog, sprint and technical documentation.
Good to have
- Hands-on experience with an enterprise AI/data platform.
- Experience in process industries such as refining, petrochemicals or chemicals; exposure to plant operations data.
- Industrial integration: DCS and historians over OPC DA/UA, edge servers, OT/IT segregation and IEC 62443.
- Familiarity with advanced process control, real-time optimization or first-principles process simulation.
- Certifications: CKA/CKAD, a cloud architect certification, or Databricks/Spark.
Working conditions
- Base location
- Kolkata
- Travel
- Frequent travel to client sites in Delhi NCR and Haryana as needed, including extended stays for deployment, commissioning and acceptance
- Reports to
- Delivery Head
- Works with
- Client Project Manager, data science lead, process engineering specialists, client IT/OT teams, PMO, Project Senior management, Customer Project POCs
- Employment
- Permanent, full-time
SPOC
Human Resources
Mail to
[email protected]
📌 Project Lead (Kolkata)
🏢 TCG Digital
📍 Kolkata