# Senior AI Engineer at Metaforms > Onsite · India · Senior · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Ametaforms%3A274f033c-5328-4a15-98e3-0c75effd3f3b (read 2026-11-11). ## Facts (observed) - Company: [Metaforms](https://alderhq.app/companies/metaforms) - Location text: Bengaluru - Where it can be done from (inferred): Onsite · India (evidence: "Location lists "bengaluru"") - Posted: 2026-06-24 (141 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/metaforms/274f033c-5328-4a15-98e3-0c75effd3f3b - Skills listed (inferred): TypeScript, Python, Go, LLMs ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Bengaluru, n 3): p25 ₹30L, median ₹35L, p75 ₹38.8L ## Description (the employer's text) ### About Metaforms Market research runs on 30-year-old survey platforms and armies of specialists hand-coding questionnaires in proprietary languages. Metaforms is the agent layer that does that work. Every survey is a program — full of skip logic, piping, quotas, and loops — and a single wrong number in a client report is unrecoverable. Our AI agents write production survey code, QA live deployments, process and clean large structured datasets, configure analysis, and generate client-ready reports, so agencies like Dynata, Savanta, and Borderless Access ship more projects with far less friction. 1,000+ surveys processed monthly Serving Fortune 500 companies across the globe Rapid month-over-month growth We’re Series A funded and scaling fast, aggressively growing our AI engineering team to build the next generation of production-grade AI agent systems. ### The Role We’re hiring a Senior AI Engineer to own the design, development, and continuous improvement of the AI agent systems that power modern research operations. This is a high-ownership, high-impact role at the intersection of applied AI and systems engineering. You’ll work on genuinely hard problems: agent reliability at scale, long-context handling, cascading error mitigation, and evaluation infrastructure — like codegen agents that write in proprietary DSLs, computer-use agents that QA live deployments, data agents that clean tabular exports and configure multi-step analysis, and evals for outputs where “correct” is genuinely ambiguous. And you’ll do it on a team that ships fast and treats quality as non-negotiable. ### What You’ll Own ### Agent Harness and Architecture Own the agent harness our production agents run on — the loop where agents plan, use tools, check their work, and recover from failures Lead research and implementation for long-context handling and cascading-error challenges in multi-step agent pipelines Drive context engineering strategy and experimentation frameworks across the team ### Evaluation and Production Monitoring Define structured rubrics for evaluating AI outputs on nuanced, ambiguous research tasks Build continuous monitoring, tracing, and failure-mode analysis for agents in production — including the loop that turns production failures into test cases Create tooling that lets domain experts refine and evolve the skill files, eval sets, and knowledge bases our agents consume ### Reliability for High-Stakes Outputs Build eval suites — regression sets, golden datasets, LLM-as-judge pipelines — that catch regressions before deploy Develop evaluation datasets for DSLs, structured data transforms, and computed outputs to systematically find and close model weaknesses Design human-in-the-loop and review workflows for outputs where a single wrong number in a client report is unrecoverable ### What We’re Looking For ### Must-Have Built and operated agentic systems in production — multi-step pipelines, tool use, codegen, computer-use, or data and reporting agents — not just prototypes 4+ years of engineering experience, with at least 1 year focused on LLM/agent systems in production Deep hands-on experience with frontier model APIs (Anthropic, OpenAI, Gemini), evaluation frameworks, and AI system optimization Strong Python skills; Go or TypeScript a plus Solid grasp of context engineering and evaluation methodology Strong instincts for debugging complex, non-deterministic system failures High ownership: you drive problems to resolution independently and pull others in when it matters ### Nice to Have Experience with LLM observability and eval tooling (Braintrust, Langfuse, LangSmith, Weave, promptfoo, or in-house equivalents) Background in semantic parsing, DSLs, or structured-output generation Prior work on computer-use or browser agents Experience with human-in-the-loop agent workflows where proposals are reviewed before apply, or agents over large structured datasets ### Why Metaforms Work at the frontier of production AI: systems handling 1,000+ research projects a month, with the reliability bar that implies A small, senior team where your decisions carry real architectural weight --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Ametaforms%3A274f033c-5328-4a15-98e3-0c75effd3f3b and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.