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Growth Path This is an individual contributor role with strong ownership expectations. High performers may be considered for workstream lead or functional lead responsibilities after approximately 12 months, based on demonstrated ownership, delivery, technical judgment, mentoring, crossfunctional influence, and ability to reduce dependency on the Director of ML. About the Role We are looking for an MLOps & Data Engineer to build the infrastructure that allows our ML team to process clinical documents, run experiments, deploy models, monitor systems, and support annotation/evaluation workflows. You will work closely with Research Engineers, ML Evaluation Engineers, Clinical AI Data Specialists, Engineering DevOps, and backend teams. This role requires both ML infrastructure and practical data engineering skills. What You Will Do Build and maintain data pipelines for clinical document processing, OCR outputs, text extraction, metadata normalization, and dataset preparation. Support deployment cycles for ML/LLM systems in collaboration with Engineering DevOps. Build and maintain training, inference, and evaluation infrastructure. Improve experiment tracking, model versioning, dataset versioning, CI/CD, monitoring, observability, and reproducibility. Build internal tools and lightweight Streamlit apps for annotation, clinical review, evaluation, QA, data inspection, and project operations. Automate recurring ML workflows and reduce manual operational burden on Research Engineers. Work with Research Engineers to productionize reliable prototypes. Work with ML Evaluation Engineers to support evaluation pipelines, hidden test set runs, regression automation, and production monitoring. Ensure systems are secure, reproducible, maintainable, and production-friendly. What We Expect 36+ years of experience in data engineering, MLOps, backend engineering for ML systems, ML platform work, or production data workflows. Strong Python skills and comfort with data processing, APIs, scripts, and internal tools. Experience with Docker, Git, CI/CD, APIs, cloud infrastructure, and production monitoring. Experience with data pipelines, workflow orchestration, object storage, databases, and batch/stream processing. Familiarity with ML workflows such as experiment tracking, model registry, inference deployment, and evaluation pipelines. Ability to build practical internal tools quickly, including Streamlit or similar lightweight apps. Strong engineering discipline: logging, tests, reproducibility, documentation, reliability, and securityaware data handling. Nice to Have Experience with LLM serving, vLLM, Ray, Triton, Kubernetes, Terraform, Airflow, Prefect, MLflow, Weights & Biases, FastAPI, Streamlit, or similar tools. Experience with OCR/document pipelines, PDFs, TIFF/JPEG processing, EHR data, or healthcare data systems. Experience working with DevOps/SRE teams and understanding where ML platform ownership should sit versus engineering DevOps ownership. Familiarity with PHI/PIIaware data handling and secure data workflows. Success in 6 Months Establish reliable ML deployment and dataprocessing workflows. Reduce RE time spent on infra, manual data preparation, and ad hoc tooling. Build useful internal tools for annotation, evaluation, review, and data inspection. Improve reproducibility of experiments and releases. Work effectively with Engineering DevOps without requiring the engineering team to own all MLspecific infra. About Triomics Triomics is building the agentic AI layer for oncology EHRs. Cancer hospitals spend billions on highly trained staff manually reading unstructured patient recordspathology reports, clinical notes, genomic panelsto power workflows like trial matching, registry curation, visit prep, and quality reporting. We replace that manual work with taskdriven AI agents that sit inside the EMR and process records at scale, in real time. Our platform is trusted by leading cancer centers including Memorial Sloan Kettering, Mount Sinai, and Yale Cancer Center. We have grown 10x in the last year and process millions of oncology medical documents monthly. Our investors include Battery Ventures, Lightspeed, General Catalyst, Nexus Venture Partners, and Y Combinator. Why Join Triomics Impact at scale. The systems your teams build directly power AI workflows that accelerate cancer research and improve patient outcomes. Cuttingedge problems. Hard, dataintensive systems at the intersection of AI, healthcare, and scale in a highly regulated industry where reliability is nonnegotiable. Worldclass team. Work alongside top talent across AI, engineering, and product, with bestinindustry compensation. Culture that ships. Fastpaced, ownershipdriven, with companysponsored workations. Perks & Benefits Lunch provided at the officeone less daily decision. Flexible working hourswe care about output, not clockins. Comprehensive health insurance for you and your family. Zomato meal benefits .
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.