Top Machine Learning Jobs in Indianapolis, IN

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Owns product lifecycle management, market analysis, customer feedback, product roadmaps, NPI processes, launches, and revenue initiatives. Uses Generative AI, LLMs, predictive analytics, and AI agents to improve research, backlog prioritization, documentation, and competitive intelligence. Collaborates with technical and business stakeholders, mentors junior employees, supports distribution strategy, delivers product training, and oversees new product development. The role is remote within the United States and requires 8+ years of experience.

Leads enterprise stress testing and quantitative risk modeling across loan, deposit, capital, and liquidity portfolios. Develops scenario analyses, forecasting models, and proprietary methodologies using statistical modeling, machine learning, Monte Carlo simulation, Python, and SQL. Evaluates model performance, supports governance and validation, provides independent challenge, and communicates portfolio risks and analytical findings to senior management, risk committees, and cross-functional partners.

Supports financial and enterprise risk management through quantitative analysis, market and liquidity risk analytics, deposit modeling, stress testing, balance sheet analysis, and automated reporting. Develops dashboards, risk indicators, forecasting tools, and quantitative models using Python, SQL, Excel, and business intelligence platforms. Researches economic trends, identifies emerging risks, improves reporting processes, and communicates analytical findings to Treasury, Finance, Credit, Risk, and executive stakeholders.

Analyzes all-source counter-unmanned-systems intelligence, movement data, geospatial information, and threat patterns to produce predictive assessments and decision-support products. Develops forecasts, models, simulations, risk assessments, and recommendations for DHS and DoD technology acquisition, capability development, sensor placement, and operational tactics. Briefs senior leaders, supports RDT&E and program activities, collaborates with government and industry stakeholders, and supervises staff as assigned. Requires active Secret clearance, Public Trust suitability, extensive intelligence analysis experience, and domestic and international travel.

Designs, develops, and maintains enterprise-scale cloud-native software for Azenta’s laboratory informatics platform. Builds microservices, APIs, event-driven systems, AI-enabled capabilities, and scalable data workflows using AWS, Kubernetes, Terraform, and PostgreSQL. Leads architecture and technical initiatives, implements DevSecOps, testing, monitoring, and security practices, and collaborates with cross-functional teams. Mentors engineers, influences technical direction, supports product delivery, and contributes to reliable, compliant, multi-tenant SaaS solutions.

Partners with Sales, Product, and Professional Services to design healthcare technology solutions, lead discovery, demos, proofs of concept, proposals, and integration approaches. Handles technical questions involving APIs, architecture, security, EHRs, HL7, FHIR, and location technologies. Creates solution documentation, supports onboarding and training, shares customer feedback with Product, and travels approximately 40% for customer visits, pilots, and go-lives.

Leads the architecture, development, and deployment of enterprise-scale AI systems and platforms. Establishes technical direction, governance, architecture standards, and responsible AI practices while modernizing legacy systems through code transformation and service decomposition. Develops AI-enabled SDLC practices, partners with engineering teams to drive adoption, and provides technical leadership and mentorship across software engineering, distributed systems, cloud, APIs, microservices, and DevOps.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Lead strategy, roadmap, and lifecycle development for enterprise biopharma platforms supporting translational research and clinical development. Define requirements, prioritize backlogs, measure adoption and reliability, ensure GxP and data privacy compliance, and align platform investments with clinical and scientific priorities. Partner with engineering, clinical, regulatory, quality, data governance, and biopharma stakeholders while mentoring product teams and promoting scalable, interoperable architectures.

Manage a portfolio of advertisers on the Adflow commerce media platform, building relationships, monitoring budgets and campaign performance, presenting business reviews, coordinating onboarding, recommending optimization strategies, and identifying revenue-growth opportunities. The role serves as the primary client contact, collaborates with internal campaign, media operations, creative, and finance teams, and uses performance marketing expertise and analytics to drive advertiser success and long-term partnerships.

Manage a portfolio of advertisers on the Adflow commerce media platform. Responsibilities include client onboarding, campaign coordination, budget and revenue pacing, performance reporting, business reviews, strategic recommendations, optimization feedback, and identifying opportunities to grow advertising spend and channel distribution. The role serves as the primary client contact while collaborating with business development, campaign strategy, creative, media operations, and finance teams.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and deploy reliable machine learning, LLM, agentic, and data-centric systems for government and enterprise customers. Build and evaluate AI agents, workflows, tool integrations, and retrieval-augmented systems; measure performance, reliability, and safety; and improve the Chariot AI operations platform. Collaborate with customers, data scientists, software engineers, and DevOps teams on mission-critical deployments. The role is fully remote or hybrid in Austin, with travel up to 20%.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads Lilly’s enterprise strategy, architecture, and deployment of agentic AI across pharmaceutical CMC development. Builds an autonomous development platform using LLMs, multi-agent orchestration, retrieval systems, knowledge graphs, digital twins, and workflow automation. Recruits and leads a multidisciplinary technical organization, partners with scientific, manufacturing, quality, regulatory, and corporate technology teams, owns ROI and adoption metrics, and drives responsible AI governance in regulated environments. The role also represents Lilly externally through industry, regulatory, academic, and technology partnerships.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.