Software Engineering Lead - Data Services

Team: IT

Location: India

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Own the architecture, development, and operation of secure, scalable backend services and RESTful APIs using Python, FastAPI, and related technologies.
• Design robust data models, schemas, access patterns, and integrity strategies across PostgreSQL, Snowflake, DynamoDB, and other data platforms.
• Build and evolve production AI/LLM services and agents with strong guarantees around performance, observability, idempotency, reliability, and cost control.
• Develop agent infrastructure including tool-calling frameworks, MCP server layers, structured-output validation, bounded agentic workflows, sandboxed execution, human-in-the-loop checkpoints, and replayable traces.
• Establish and maintain AI evaluation frameworks, including scorers, labelled datasets, trajectory and multi-turn evaluations, regression suites, model calibration, and drift detection.
• Set engineering standards for scale and reliability, including caching, rate limiting, circuit breakers, bounded retries, graceful degradation, cost ceilings, and high-availability practices.
• Define observability standards through structured tracing, performance metrics, cost-per-request monitoring, dashboards, and actionable operational insights.
• Lead two engineering pods through their respective leads, while conducting architecture and code reviews and raising standards for quality, testing, and AI-native development.
• Partner with Data Platform, Application, and Product teams to translate ideas into architecture decisions, prototypes, and production-ready solutions.
• Promote a fast, outcome-oriented engineering culture through hands-on technical leadership, reference implementations, mentorship, and continuous improvement.

Requirements

• 7–10 years of hands-on backend engineering experience, including experience leading engineers or engineering leads, with at least 3 years of strong hands-on Python development.
• Expert-level Python and substantial production experience with FastAPI, Pydantic, and modern backend engineering practices.
• Proven experience building and operating large-scale production systems handling millions of API calls and understanding the challenges that emerge as systems scale.
• Demonstrated experience shipping AI/LLM services, APIs, and agents into production, including building evaluation systems to maintain reliability and trustworthiness.
• Strong hands-on experience with AI harness engineering, including tool-calling frameworks, MCP servers, agent runtimes, evaluation harnesses, and reusable infrastructure for other engineers.
• Solid data-modelling expertise across both SQL and NoSQL databases, together with experience designing and consuming RESTful services at scale.
• Practical experience using agentic development environments such as Claude Code, Cursor, or equivalent tools as part of your normal engineering workflow.
• Strong technical leadership skills and a hands-on approach, with the ability to move from architectural design and whiteboarding through reference implementation and team adoption.
• Experience with AI evaluation and observability technologies such as Braintrust, Inspect AI, Promptfoo, Logfire, or OpenTelemetry is highly valued.
• Familiarity with agent frameworks and protocols such as Claude Agent SDK, Pydantic AI, LangGraph, or MCP is advantageous.
• Experience working with AWS and/or GCP at scale, including technologies such as ECS/Fargate, Lambda, S3, RDS, and DynamoDB.
• Background in B2B data, entity resolution, high-throughput enrichment pipelines, Snowflake, or modern data-platform tooling is a plus.
• Startup or scale-up experience, with a track record of shipping quickly and taking end-to-end ownership of outcomes, is strongly preferred.

Benefits

• Fully remote opportunity based in India.
• Competitive base compensation.
• Meaningful equity participation and the opportunity to share in the organization’s growth.
• Significant architectural autonomy and ownership of core backend, data, and AI systems.
• Hands-on technical leadership, allowing you to continue building while influencing engineering direction.
• Opportunity to lead and grow two engineering pods through their team leads.
• Work in an AI-native environment where agentic development, AI-assisted testing, automated review pipelines, and built-in evaluations are core engineering practices.
• Exposure to high-scale systems, modern cloud infrastructure, AI/LLM platforms, and data technologies.
• Lean, senior engineering teams with minimal organizational layers and a strong focus on ownership and rapid delivery.
• The opportunity to build systems that directly impact customers at global scale.