• design, prototype, implement, and maintain scalable AI systems powered by LLMs and Foundation Models, • build agentic AI applications — multi-step LLM workflows with RAG, tool/function calling, and structured outputs, • design and run evaluation pipelines to measure and improve agent quality (LLM-as-judge, regression testing, safety/red-team evaluation), • collaborate with frontend, product, and other engineering teams to design API contracts and deliver end-to-end features, • monitor the performance, latency, cost, and SLAs of AI systems in production, • provide technical leadership and drive the necessary infrastructure or architectural changes.

Title: Manager, Data Science (ML Focus) - Remote in the US Location: Indianapolis, IN, USA Department: PS_Data Science Team Job Description: - temprop="jobLocation" itemscope="" itemtype="http://schema.org/Place">Employees can work remotely - temprop="employmentType">Full-time - Department: PS_Data Science Team - Business Unit: Professional Services Company Description We are a passionate team of 300+ engineers, mathematicians, data analysts, project managers, and business consultants. But more importantly, we are active listeners, deep thinkers, and courageous problem solvers. The Resultant team purposefully comes together to produce a positive outcome. Our name symbolizes our commitment to empathy and collaboration—of not just delivering our clients with the best solutions, but to deeply listening to them, understanding their needs, and learning from each other in the process. The force of Resultant comes from the combined knowledge, passion, and innovation of our team and partners. Together, we partner with clients in the public and private sectors to help them overcome their most complex challenges, empowering our clients to drive meaningful change in their organizations and communities. In everything you do, you’ll help your clients, colleagues, and communities thrive. Resultant was founded as KSM Consulting in 2008. Job Description At Resultant, we are fearless problem solvers.  We are passionate about helping our clients solve their toughest problems, while empathizing with people our solutions will help.  Data analytics is a core component of how we do this.  We are looking for a Senior Data Scientist / Machine Learning Engineer (DS/MLE) to add to our talented team of team of Data Scientists, Data Engineers, and Data Architects to solve some of the most exciting and challenging problems faced by companies and governments in today’s fast-paced environment. As a Senior DS/MLE, you will have the opportunity to develop novel machine learning and AI solutions and deploy them to production applications and systems.   Here is what a typical day for you might look like:   - Engaging directly with current and prospective clients to understand business needs, translate them into technical requirements, and communicate findings in a clear, actionable way - Partnering with internal and client stakeholders to shape solutions, develop proposals, and contribute to go-to-market initiatives - Design, develop and deploy efficient data pipeline for both structured and unstructured data  - Productionize machine learning research in both on premises and cloud environments  - Rapid prototyping solutions and integrating of new data sources  - Drive optimization, testing and tooling to improve quality of solutions  - Develop and apply machine learning and advanced analytics algorithms to solve problems you have never seen before   - Collaborate with data architects and software developers to plan and construct the architecture of model deployment   - Develop and support APIs to serve models and other data solutions - Continuously learning and adopting new tools and technologies to stay ahead in a rapidly evolving AI landscape Qualifications *This is not an entry level/recent graduate role and requires at least 5+ years of related industry experience.* Education and Experience: Required - Experience working in a consulting firm (professional services or managed services) - Previous experience managing a team of Data Scientists. - Bachelor’s degree (and preferably an advanced degree) in Computer Science, Engineering, or in a quantitative field is required   - 5+ years experience with software engineering in Python (additional languages like Scala/C++/R are a plus)  - 3+ years experience with Databricks and/or Apache Spark  - 3+ years experience with advanced analytics and modern machine learning techniques  - 3+ years experience with APIs, containerization (Docker), and orchestration (Airflow)  - 3+ experience with relational databases and SQL (Postgres, Redshift, SQL Server)  - 3+ experience with Cloud platforms (Azure, AWS, GCP) and services - Proficient with version control   - Proficient in software architecture and design patterns  - Proficient on Linux for both development and operations  Preferred - Experience with deploying applications on Kubernetes - Experience with Palantir Foundry - Experience with Python API development frameworks (FastAPI, Django, Flask) - Experience with NoSQL and streaming platforms, e.g., Kafka, MongoDB, Neo4j Consulting, Communication and Leadership Skills: - Experience working in a consulting or client-facing environment, collaborating directly with business stakeholders - Strong ability to translate complex technical concepts for non-technical audiences - Demonstrated experience contributing to business development efforts, including proposal writing, solutioning, and partnering with sales or account teams - Solution creativity and effective written, verbal, and presentation skills - Proven success working across diverse industries and project sizes with a collaborative, outcomes focused mindset - Entrepreneurial inclination to discover novel opportunities for applying analytical techniques to business and social problems  Additional Information What It's Like to Work Here At Resultant, we are driven by purpose—partnering with clients to take on their toughest challenges and creating outcomes that make a real difference. Success here means lasting impact, not just delivered projects. We work as collaborative experts, leaning into complexity with confidence and humility, asking smart questions, sharing ideas freely, and combining our diverse expertise to turn challenges into clear, transformative solutions. We make our outcomes visible by sharing results, stories, and lessons learned, because growth happens through shared experience. Accountability & Ownership Resultant offers a flexible, high-trust environment, paired with a shared commitment to accountability: - Take ownership of your work from start to finish and deliver on your commitments to clients and coworkers - Communicate proactively, especially when priorities shift - Focus on outcomes, follow-through, and measurable impact - Show up where it matters, in person or virtually, when it strengthens relationships or results - Do the small things brilliantly: respond timely, stay organized, and follow through to build trust We trust each other to own our work and deliver exceptional results. Flexibility means autonomy paired with accountability: the freedom to work in ways that fit your life, balanced with the reliability our clients and coworkers count on. Is Resultant the right spot for you? You may thrive here if you bring curiosity and a consulting mindset to every challenge, take ownership of your growth, and give and receive feedback with intention. If you're energized by bold ideas, continuous learning, and investing in the people around you, this is your place. At Resultant, we show up for each other, own our outcomes, and build leaders who create lasting impact for our clients, our communities, and each other.

• Develop and implement a comprehensive MLOps strategy that enables the seamless integration of machine learning models into our environment. • Design, implement, and maintain scalable and automated MLOps pipelines (data ingestion, training, evaluation, deployment, and monitoring). • Build internal tools or integrate existing solutions for versioning, model registry, CI/CD, and observability. • Collaborate with cross-functional teams to design, deploy, and manage scalable infrastructure for machine learning workloads. • Own the full development lifecycle from design to incident response. • Work closely with data scientists, software engineers, and other stakeholders to understand model requirements, deployment needs, and data dependencies. • Enhance fraud detection systems using machine learning

• Architect and evolve our multi-agent orchestration platform (currently built on Hermes / Multica) • Design and implement voice AI pipelines — STT, real-time TTS with streaming, VAD, SIP/RTP telephony integration • Build and maintain RAG pipelines with retrieval quality measurement, re-ranking, and hybrid search over vector + keyword indexes • Fine-tune and evaluate LLMs for domain-specific tasks including customer support, classification, and structured extraction • Own the AI observability stack: tracing, instrumentation, cost tracking, and quality regression alerting • Define and enforce guardrails: hallucination detection, PII redaction, output safety scanning, and rate-limiting across multi-tenant deployments • Build data ingestion, preprocessing, and feature pipelines supporting model training and continual learning • Set architectural standards for AI systems across the group; conduct design reviews and own ADRs for major decisions • Mentor ML engineers and applied scientists; grow the team's capabilities in production AI • Engage with external research partners and track emerging work to identify signals worth productionizing