You will develop and implement KPI’s and targets covering complete Transformer portfolio and provide visibility of activity and outcomes in the dimensions listed above, the metrics for her/his performance review might include the number of CTT’s, HIIT’s, specifications influenced.
You will contribute to and follow a structured market plan combining the technical info gathered with customers while deploying a strategic overview mindset, provide customer feedback as well as strategic marketing inputs to Global and Regional Product Managers and support the market analysis, validation, competitive landscape and heat maps.
You will be responsible to ensure compliance with applicable external and internal regulations, procedures, and guidelines.
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POC & demonstrations by lead and validate complex Proof of Concepts (POC), technical demonstrations, and site surveys to ensure solutions align with customer requirements and business objectives.
Vendor compliance, manage the team’s vendor certification roadmap to ensure the organization maintains necessary partner statuses and meets all technical requirement thresholds.
Business growth by collaborating with product team to design marketing programs and involves in meeting and quarterly sales quotas like QBR through technical leadership.
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Support Project Managers, Sales and Proposal teams during bidding, project pursuit and execution by providing mechanical engineering input, technical review and risk assessment.
Provide technical guidance and mentorship to junior engineers, designers and project team members. Principal level candidates may also support regional technical leadership and engineering standardization.
Lead and execute mechanical and piping engineering activities for custody transfer metering systems, allocation metering systems, analyzer systems, sampling systems and packaged skid solutions.
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You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
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You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
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Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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Manage SageMaker environments (Studio, Canvas, Notebooks) for enabling multi-domain ML use cases.
Operate DataPipes (EKS + Airflow) for ingestion orchestration, ensuring high availability and version-controlled configurations via IaC (CloudFormation/CDK).
Maintain & enhance logging, monitoring, observability & DevOps automation via modern tools such as CloudWatch, Splunk, PagerDuty, Slack, ServiceNow, Snowflake observability features.
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