Senior Machine Learning Engineer

We need to output HTML fragment with sections: About the role? Actually we have sections: About Xenon7, Job Summary, Key Responsibilities, Requirements, etc. Use

for section headings. Do not repeat job title. Do not include Job ID etc. Remove boilerplate. Must include only sections with real content. Provide

• for lists. Use

  for prose. We need to parse the posting. Let's extract content: Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) About Xenon7: paragraph. Job Summary: paragraph. Key Responsibilities: bullet list (multiple lines). We'll need to format as

  • . Requirements: includes Experience & Mindset (subheading?), Must-Have Technical Stack, Domain Competency, Nice-to-Haves & Certifications, What This Role Is NOT. We need to decide sections: maybe "About Xenon7", "Job Summary", "Key Responsibilities", "Requirements". Within Requirements we could have sub-sections? But instruction: use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should stick to those generic headings. However we can also use other headings if they match content? The instruction says use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It suggests those are examples; we can use those headings if appropriate. Probably we should use those standard headings: About the role (maybe About Xenon7?), Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. But we need to include only sections with real content. The posting does not mention Benefits, Pay, Schedule explicitly (though schedule is hybrid 3-day onsite). Actually schedule is mentioned: "Operating in a 3-day onsite hybrid capacity". That's schedule info. So we could include a Schedule section. Pay not mentioned. Benefits not mentioned. Thus we can have sections: About the role (maybe About Xenon7), Responsibilities, Requirements, Skills (maybe technical stack), Schedule. But we need to avoid filler like "commensurate with experience". Not present. Let's craft:

    About the role

    At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.

    But "About the role" maybe should be about Xenon7? Could also be "About Xenon7". However instruction says use

    for section headings (e.g. About the role, Responsibilities, Requirements...). So we can use "About the role" heading and put Xenon7 description there. Job Summary: maybe part of About the role? Could be separate section "Job Summary"? Not in list but could be considered part of About the role. But we can include as a paragraph under About the role. Better: Use headings: About the role, Responsibilities, Requirements, Skills, Schedule. Now extract content: About the role: combine Xenon7 description and Job Summary. Xenon7 paragraph: "At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth." Job Summary paragraph: "We are seeking a Senior Machine Learning Engineer with extensive experience in production MLOps, model deployment, and system scaling to drive ML engineering initiatives for a top-tier life sciences client. This role sits at the critical intersection of production ML infrastructure, life science research, and manufacturing process engineering. In this position, you will own the architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines. You will bridge complex domains—from computational biology, small and large molecule research, and clinical trial analytics to active pharmaceutical ingredient (API) manufacturing processes, batch optimization, and industrial automation ML. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, life science researchers, and platform engineering teams to build robust, low-latency ML systems that scale across the enterprise." We can combine into one

    or two

    . Probably two

    under About the role. Responsibilities: list bullet points from Key Responsibilities. Let's extract bullet points: - Production MLOps & Systems Architecture: Design, deploy, and maintain robust, production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring - Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments - Process Engineering & Manufacturing ML Integration: Operationalize and integrate production ML models into operational technology (OT), API manufacturing workflows, and chemical process control systems - Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases - Scalable Inference & System Integration: Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe) for model deployment into live production applications - Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, and modern pipeline engines (Kubeflow, MLflow) - Technical Leadership & Domain Alignment: Partner directly with chemical engineers, computational biologists, and software architects to translate operational friction into production-ready ML engineering solutions - Establish enterprise MLOps standards, model governance, and CI/CD best practices across the full machine learning operational lifecycle We'll make each as

    . Requirements: need to capture Experience & Mindset, Must-Have Technical Stack, Domain Competency, Nice-to-Haves & Certifications, What This Role Is NOT. We can break into sub-sections? But we only have headings for Requirements (maybe includes all). Could also have Skills heading for technical stack. Let's think: The instruction says use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have Requirements and Skills separate. Thus:

    Requirements

    containing experience, mindset, location, work auth, culture, domain competency maybe.

    Skills

    containing Must-Have Technical Stack (languages, frameworks, MLOps, infrastructure, monitoring, domain competency). Also Nice-to-Haves & Certifications could be part of Skills or separate? Could be under Skills as well. What This Role Is NOT could be a note under Requirements or Skills? Might be better as a separate section maybe "Role Clarification" but not in list. Could be included under Requirements as a paragraph. We'll decide: Requirements: Experience & Mindset, Location & Work Auth, Culture & Communication, Domain Competency (Scientific & Process Focus), What This Role Is NOT. Skills: Must-Have Technical Stack, Nice-to-Haves & Certifications. Now extract details. Experience & Mindset: - Experience: Senior-level proficiency (10-20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling - Domain Adaptability: Demonstrated ability to deploy and maintain production ML systems across non-standard, highly specialized domains (e.g., transition between process/chemical engineering ML and clinical/scientific research applications) - Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area - Culture & Communication: Pragmatic problem-solving mindset, strong collaborative drive, and the ability to articulate complex MLOps architecture to cross-functional engineering teams Domain Competency (Scientific & Process Focus): Deep exposure to applying ML models in either scientific/clinical domains (drug discovery, small/large molecule, computational biology) OR chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization) What This Role Is NOT: - Not a Data Scientist or Exploratory R&D Specialist: You will not be focusing on exploratory data analysis, academic algorithms, or standalone Jupyter notebook modeling; you are building, scaling, and maintaining production MLOps pipelines, inference engines, and model integration code - Not a non-coding Architect: This is a 100% hands-on MLOps and software engineering lead role requiring direct model deployment, infrastructure creation, and technical execution - Not a Fully Remote Position: This role requires a steady hybrid commitment of 3 days onsite per week at the client site in Indianapolis Now Skills: Must-Have Technical Stack: Languages & Frameworks: Advanced Python, C++, and deep proficiency with PyTorch, TensorFlow, or Scikit-learn MLOps & Serving: Proven expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime Infrastructure & Orchestration: Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and cloud platform ecosystems (AWS/Azure) Monitoring & Integration: Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines Nice-to-Haves & Certifications: - Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline - Direct experience operationalizing ML models inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors - Certifications: AWS Certified Machine Learning - Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials Schedule: we have hybrid 3-day onsite. Could be under Schedule section. Schedule: Operating in a 3-day onsite hybrid capacity in Indianapolis. Now produce HTML. We must not include any extra text outside HTML fragment. No markdown fences. Let's craft:

    About the role

    At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.

    We are seeking a Senior Machine Learning Engineer with extensive experience in production MLOps, model deployment, and system scaling to drive ML engineering initiatives for a top-tier life sciences client. This role sits at the critical intersection of production ML infrastructure, life science research, and manufacturing process engineering. In this position, you will own the architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines. You will bridge complex domains—from computational biology, small and large molecule research, and clinical trial analytics to active pharmaceutical ingredient (API) manufacturing processes, batch optimization, and industrial automation ML. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, life science researchers, and platform engineering teams to build robust, low-latency ML systems that scale across the enterprise.

    Responsibilities

    • Production MLOps & Systems Architecture: Design, deploy, and maintain robust, production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring
    • Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments
    • Process Engineering & Manufacturing ML Integration: Operationalize and integrate production ML models into operational technology (OT), API manufacturing workflows, and chemical process control systems
    • Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases
    • Scalable Inference & System Integration: Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe) for model deployment into live