Software Engineering Lead - Data Services

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineering Lead - Data Services based in India.
This is a high-impact technical leadership role responsible for the backend, APIs, data services, and AI/LLM systems powering a global B2B sales intelligence platform. You’ll lead two engineering pods through their team leads while remaining deeply hands-on, with approximately 80% of your time focused on architecture and engineering. You’ll shape scalable services, production-grade AI agents, data models, and reliability standards that reach customers every day. The environment is AI-native, fast-moving, and built around senior engineers with strong ownership. You’ll have significant architectural freedom to define the next generation of backend and AI infrastructure. This is an opportunity to lead from the front, combining technical depth with broad organizational influence.
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.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Work arrangement
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