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

• 10-15 years of experience in data management, with at least 10 years in a similar role.
• Proven experience leading teams and projects in data management.
• Strong proficiency in big data technologies (e.g., Spark, Kafka), SQL, and data warehousing solutions.
• Experience with ETL and ELT tools and processes.
• Proficiency in data modelling and data warehouse / data lake / data lakehouse concepts.
• Knowledge and experience of data quality concepts and metadata management.
• Understanding of data governance principles and best practices, like GDPR and HIPAA.
• Good understanding of product lifecycle and roadmap planning
• Experience with large product or services companies is preferred.

Roles & Responsibilities

Key Responsibilities

• Drive and deliver the design, development, and deployment of our data management and storage related modules in mcube.
• Oversee data architecture and data integration processes across modules.
• Enablement of real-time and batch ETL and ELT processes, ensuring data quality, performance and optimal usage of appropriate storage technologies.
• Ensure the platform’ data management and storage modules enable our customers to follow their data governance principles and best practices, including authorization, logging and data security.
• Ensure the platform has consistent and secure data access controls, data masking, and encryption processes.
• Establish and develop/integrate a central technical and business meta data layer leveraging modern, knowledge graph and AI based technologies
• Lead a team of developers to deliver above capabilities and features in a matrix environment
• Partner with product management to ensure alignment with market and customer trends.
• Partner with QA to deliver industry standard software quality

Other Information

• Bachelor’s or Master’s degree in Computer Science or a related field (BTech preferred).

Skills Required

• Expertise with at least one major cloud platform (AWS, Azure, GCP).
• Hands-on experience with monitoring and observability tools (CloudWatch, Datadog, New Relic, Prometheus).
• Strong knowledge of Infrastructure as Code (IaC) (Terraform, CloudFormation, ARM templates).
• Experience with incident management frameworks (ITIL, SRE principles, PagerDuty/On-Call rotations).
• Understanding of container orchestration (Kubernetes, ECS, AKS, GKE) and CI/CD pipelines.
• Familiarity with cloud security best practices and compliance frameworks.

• Proven ability to lead and motivate teams in a fast-paced environment.
• Excellent problem-solving, decision-making, and communication skills.
• Ability to collaborate with both technical and business stakeholders.

Roles & Responsibilities

Key Responsibilities

• Own day-to-day operations of all production, staging, and development cloud environments.
• Ensure high availability of services by maintaining robust monitoring, alerting, and incident response processes.
• Lead root cause analysis (RCA) and post-mortem reviews to drive continuous improvement.
• Implement observability practices (logging, tracing, metrics) to proactively detect and resolve issues.
• Oversee patch management and maintenance activities to keep systems secure and up-to-date.

Automation & Optimization

• Develop and maintain automation scripts for cloud resource provisioning, scaling, and monitoring.
• Optimize cloud cost usage through rightsizing, reserved instances, and cost governance policies (FinOps).
• Standardize runbooks and operational playbooks for common tasks and incidents.

Security & Compliance

• Enforce security baselines (IAM, encryption, network segmentation) across all cloud services.
• Collaborate with security teams to implement cloud-native security tools and respond to threats.
• Ensure compliance with regulations and audits (SOC 2, ISO 27001, GDPR, HIPAA where applicable).

Team Leadership & Collaboration

• Lead, mentor, and develop a team of cloud operations engineers.
• Foster a culture of SRE/DevOps best practices, promoting automation and reliability.
• Work closely with application, DevOps, and networking teams to support business-critical services.
• Act as escalation point for critical incidents and operational challenges.

Vendor & Stakeholder Management

• Manage relationships with cloud providers (AWS, Azure, GCP) and monitoring tool vendors.
• Provide regular status updates and operational metrics to senior management.
• Collaborate with finance to align on cloud cost forecasts and budget planning.

Other Information

• Bachelor’s degree in Computer Science, IT, or related field.
• 5–8 years of experience in cloud operations, site reliability engineering (SRE), or IT infrastructure.
• 2+ years in a leadership role managing operational 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 Life Sciences problems
• 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, analytical formulation) for multiple client engagements in Life Sciences domain.
• 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

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

• Project Management Experience on development projects with Java, Spring, and Angular.
• Hands-on work experience in micro service base architecture.
• Understanding of cloud providers and integrations (AWS/Azure/GCP)
• Relevant Experience (Experience in managing mid size projects with at least 6 people) should be at least 5 years, overall work experience would be 10+ years
• Research and evaluate hardware and software technology options.
• Responsible for management of a multiple Projects and or Large Programs for App Dev of high complexity, priority, and risk
• Develop a detailed estimate of the level of effort, schedule and budget necessary to successfully develop and deliver the solution meeting the requirements and quality expectations
• Develop an initial plan to clearly define the project requirements and determine an appropriate solution design
• Escalate problems as necessary to ensure awareness and seek assistance with resolution when necessary
• Meet the project requirements by delivering a quality solution, on time, and within the planned budget
• Create and manage all the accepted project management deliverables including the project charter, project plan, budget, resource plan, risk plan, support plan, implementation plan and other deliverables as required
• Track, manage, and adjust the original plan as necessary to ensure successful delivery
• Provide regular status reports to stakeholders, sponsors, the enterprise, and others involved in the project
• Effectively coordinate with the various technology, business, and vendor teams to ensure the project’s success

• Experience of 7 – 9 years in Business Analyst role in relevant domain in manufacturing, supply chain and/or finance
• Perform subject matter expert role for multiple functional areas like Manufacturing & Operations,  Supply Chain & Procurement , Logistics & Finance
• Deep functional knowledge and experience with working in industry domains preferably oil & gas, utilities or manufacturing 
• Lead the functional discussions with the client to develop the functional requirement specifications for applications and business automation & reporting solutions
• Knowledge of Software Development Lifecycle methodologies and frameworks including Agile and iterative development
• Play a lead role during User Acceptance Testing (UAT) and training for successful deployment of business solutions 

• AWS Knowledge  (must have) – Lambda, AppSync, Dynamo DB
• JS (must have)  – Node.JS

• AWS Knowledge  (preferred to have) – API Gateway, EC2, Step Function, Cloudwatch, EKS. CloudFormation
• Language (good to have) – Java , J2EE, JMS, Hibernate, Micro Service, Spring boot, Spring Cloud, Spring Integration.
• Other (good to have) – JMS(Like MQ, Tibco), Kafka/Kinesis
• Database (good to have) – Oracle, Any No SQL DB like Couchbase, MongoDB
• JS (good to have)  – Angular/React JS
• Must be a developer who has built the solutions using Java before and now providing solutions using AWS/Java/JS.