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Machine Learning Engineer Responsibilities Evaluation — the offline and online eval suites that tell us whether the agent is actually getting better: golden datasets from real traffic, calibrated LLM-as-judge rubrics, and eval as a release gate for every model, prompt, or pipeline change. Model strategy — deciding which model serves which step, and building the router that balances quality, latency, and cost. Benchmarking new releases against our own evals rather than vendor claims. Fine-tuning and adaptation — SFT, LoRA, and preference tuning on automotive-domain tasks when it genuinely beats better prompting or retrieval, plus the data flywheel that feeds it. The deep research pipeline — multi-step retrieval and synthesis across inventory, specs, pricing, reviews, and ownership cost, with grounding and citations we can trust. Serving and lightweight MLOps — inference services, embedding and index refresh jobs, versioning, staged rollout and rollback, and the observability to see quality and cost in production. Fallback and redundancy — multi-provider fallback chains, circuit breakers, and graceful degradation, so a slow or unavailable model never becomes a broken experience for the buyer. Cost-conscious infrastructure — owning cost per conversation and keeping the stack lean. What we’re looking for Required 3+ years building and shipping ML or LLM systems in production (not just notebooks or POCs). Fine-tuning experience (LoRA/QLoRA, SFT, DPO) and familiarity with serving stacks like vLLM, TGI, or Ollama. Experience with agent frameworks and tool-calling architectures, and their failure modes. Experience with LLM observability tooling (Datadog LLM Observability, etc.). Multi-provider LLM operations and resilience patterns at real traffic volume. Strong Python; comfortable owning a service end to end, not handing it off. Real, hands-on experience evaluating LLM systems — you’ve built an eval set, argued about a rubric, and caught a regression before users did. Practical experience with retrieval systems: embeddings, vector stores, hybrid search, reranking. Solid engineering fundamentals: APIs, async, testing, CI/CD, containers, cloud. Cost-awareness as an instinct. You reach for the cheapest thing that meets the bar. Advanced English for conversation with global teams. Nice to have Familiarity with tools and practices such as Unsloth, Hugging Face, MLflow, LangSmith, and/or Weights & Biases. Marketplace, e-commerce, recommender, or search ranking background.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.