Careers

Skills Required

• 6 – 10 years of product management experience, with ownership of customer-facing platforms or APIs.
• 3+ years shipping and operating AI/ML-powered products in production.
• Strong understanding of LLM-based systems, RAG architectures, and AI ecosystem.
• Knowledge of Agent development frameworks and MCP.
• Ability to reason about trade-offs between accuracy, latency, cost, and UX.
• Comfortable reviewing prompts, experiments, dashboards, and system metrics.
• Strong communication skills in highly technical and ambiguous domains.

Nice to Have

• Experience with semantic technologies such as knowledge graphs, ontologies, or semantic data models.
• Experience building AI platforms for enterprise customers.
• Familiarity with AI governance, compliance, or regulated environments.
• Exposure to UX patterns for AI-driven workflows.

Key Responsibilities

• Own the vision, roadmap, and success metrics for the AI platform.
• Define core platform capabilities.
• Enable customer and internal teams to build AI use cases using scalable platform primitives.
• Prototype AI workflows and reference implementations to validate new capabilities.
• Define and track KPIs for AI quality, latency, reliability, cost, and adoption.
• Embed enterprise-grade guardrails for security, privacy, and safe AI usage.
• Influence (not solely own) decisions e.g. model selection, buy vs build, risk trade-offs, etc
• Act as the product interface across engineering, ML, security and go-to-market teams.

Skills Required

• Expert knowledge of data science algorithms, methodologies, and end-to-end lifecycle (AI/ML/GenAI), MLOps and post deployment service of AI models
• Strong comprehension of complex Oil & Gas and downstream Petrochemical industries problem.
• Exceptional articulation skills to communicate technical concepts clearly to both technical and business stakeholders.
• Advanced analytical skills for strategic problem-solving and actionable insight generation.

• Exposure to cloud platforms (AWS/Azure/GCP), DevOps, deployment and scaling frameworks
• Experience in leading agile teams and agile methodology.
• Familiarity with data visualization tools (e.g., Tableau, Power BI).
• Understanding of data engineering principles and big data.

Roles & Responsibilities

• Lead data science efforts (AI, ML, GenAI, optimization) for multiple client engagements in n Oil & Gas and downstream Petrochemical industries.
• Manage end-to-end Data Science project lifecycle from problem comprehension, analytical solution development to final delivery.
• Act as primary point of contact for client interactions, presentations, and requirement elicitation.
• Guide teams on advanced data science techniques ensuring alignment with business objectives.
• Collaborate cross-functionally with domain experts, business stakeholders, and technical teams to deliver robust analytical solutions.

Other Information

Role Summary

Key Responsibilities

• Own a named account portfolio; build and execute a multi-year pipeline strategy aligned to company revenue targets.
• Develop and maintain senior-level relationships (C-suite, VP Operations, CIO, CDO, COO) across target accounts, positioning TCG Digital as a trusted transformation partner, not just a vendor.
• Lead complex, consultative sales engagements end-to-end from opportunity qualification and needs discovery through solution development, proposal, commercial negotiation, and contract execution.
• Collaborate with pre-sales and solutions architecture teams to design compelling, client-specific solution proposals that tie our platform capabilities to measurable business outcomes.
• Build and maintain accurate pipeline forecasts in CRM; provide regular, transparent reporting on deal status, risks, and revenue projections to sales leadership.
• Drive account expansion within existing clients by identifying upsell and cross-sell opportunities across TCG Digital’s full portfolio of solutions.
• Represent TCG Digital at industry events, conferences, and airline forums to build market presence and generate qualified pipeline.
• Maintain deep, current knowledge of the competitive landscape and articulate TCG’s differentiated value clearly.
• Serve as the voice of the customer internally, conveying market feedback to product, delivery, and leadership teams to shape roadmap priorities and go-to-market strategy.

Required Qualifications

• 7+ years of enterprise B2B sales experience, with a demonstrated track record of closing multi-stakeholder opportunities.
• Direct experience selling to airlines. This is a hard requirement. You must understand airline buying process.
• Proven ability to navigate complex sales cycles (6 – 9 months) involving multiple decision-makers and procurement processes.
• Ability to establish and nurture senior relationships (VP-level and above) that you can engage regularly.
• Strong command of consultative selling methodologies (MEDDIC, Challenger, Solution Selling, or similar).
• Ability to craft and deliver executive-level presentations and business case narratives that connect operational pain points to technology solutions.
• Experience working collaboratively with pre-sales, solutions architecture, and professional services teams.
• Comfort operating in a fast-moving, entrepreneurial environment.
• Bachelor’s degree in Business, Engineering, Aviation Management, or a related field; MBA is a plus.
• Ability to travel frequently for client engagements and industry events across the EMEA region and elsewhere, if necessary.

Scope of work

Responsibility

• Collaborate closely with the sales team to understand customer needs, pain points, and strategic plans.
• Meet prospective and existing customers to build relationships and gather insights for tailored solutions.
• Act as a technical expert, presenting complex solutions to clients and stakeholders.
• Design and architect system integrations and IT solutions that leverage AI technologies.
• Stay current with AI trends, large language models (LLMs), and related platforms to enhance offerings.
• Prepare detailed IT architecture diagrams using the latest tools.
• Develop and deliver engaging presentations that showcase our solutions.
• Support RFP responses and create technical documentation as needed.
• Be prepared to travel up to 15 days a week to meet clients and support sales initiatives.
• Mentor junior presales team members and contribute to knowledge sharing.
• Proficient in making presentations and creating detailed IT architecture diagrams.
• In-depth knowledge of AI trends, use cases, LLMs, and AI platforms.
• Excellent verbal and written communication skills in English.
• Strong problem-solving abilities and innovative thinking. Proven track record in building relationships with clients and stakeholders, with a knack for teamwork.

Other Information

• Minimum 20 years of experience in presales and enterprise architecture within IT sales and systems integration.
• Bachelor’s degree in Engineering or a related field; advanced degrees preferred.

Role summary

Key responsibilities

• Sales-Delivery Collaboration
  • Receive formal handover from the pre-Sales team covering SOW, pricing model, delivery estimated with assumptions, risk register, and client relationship notes.
  • Validate that delivery commitments are achievable;
  • Flag gaps or risks before baseline is set.
  • Help establish the scope baseline prior to kick-off.
  • Provide contract, Pricing Sheet, P-o-S Factory estimates, stakeholder map and risks/issues/dependencies/assumptions to Delivery for triggering Project initiation into Delivery
  • Conduct external kick-off with customer, working with Pod team to develop and finalize kick-off content
• Commercial Accountability
  • Ensure invoices are raised on time per contractual milestones or periodic billing schedules.
  • Track billing triggers — milestone completions, delivery acceptances, T&M cycles.
  • Coordinate with Finance and client-side teams for invoice approval and payment.
  • Monitor accounts receivable and escalate overdue invoices within defined thresholds.
  • Maintain commercial hygiene — no revenue leakage through missed billing cycles or unclaimed change requests.
  • Ensure client approval of invoices is obtained in time.
  • Drive resolution of dependencies that affect invoicing and commercial closure.
• Scoping & Change Management
  • Maintain the agreed project scope baseline and identify scope creep early.
  • Communicate change implications clearly to both client and delivery team before work proceeds.
• Client Governance & Reporting
  • Establish the value note – baseline “as-is” state of the defined value parameters and the “to-be” state of the value parameters as success criteria for the Project
  • Collaborate with the Project Lead to ensure deliverables serve the client’s strategic intent, not just SOW compliance.
  • Conduct formal value realization reviews with the client at key intervals.

Qualifications and experience

• B.E./B.Tech./MCA; PMP, PRINCE2 or SAFe certification preferred.
• Work experience in process industries (refining, petrochemicals, chemicals).
• A total of 10-15 years of experience in software delivery, with last 3-5 years as Project Manager for software, data or AI projects.
• Full SDLC experience (requirements, design, development, testing, release, support) in agile (Scrum/Kanban) and hybrid models.
• Customer Engagement, Stakeholder Management.
• Project management discipline: scope, schedule, cost, risk, quality and change control; estimation with WBS and story points.
• Jira and Confluence: backlogs, sprint boards, dashboards and workflows; MS Project or equivalent.
• Fixed-price delivery: change requests, milestone billing and margin management.
• Strong client communication and stakeholder management with senior customer teams.

• Degree in Chemical, Instrumentation or Process Engineering.
• Delivery of advanced process control, real-time optimization or plant analytics projects, including site commissioning and acceptance tests.
• Knowledge of first-principles process simulation and hybrid (physics plus ML) models.
• Industrial data integration: DCS and historian data over OPC, OT/IT network segregation, IEC 62443.
• Experience with large public-sector or energy clients.
• Software engineering practices: Git workflows, code review, CI/CD pipelines (Jenkins/GitLab), automated testing, environment and release management.
• Delivery of data, analytics or AI solutions: data pipelines, ML model lifecycle (MLOps), APIs and microservices, dashboards.
• Working knowledge of infrastructure: Linux, containers and Kubernetes, virtualization, application security and VAPT.

Role summary

Key responsibilities

• 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

• 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

• 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.

• 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

• 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