Engineering-Applied Science/Machine Learning/Data Science

We are seeking a highly accomplished leader in Applied AI and Machine Learning to drive Tekion’s end-to-end AI strategy, research innovation, and production-scale ML platform execution. This role combines deep scientific expertise with strong systems and platform engineering capabilities to translate advanced ML and LLM research into reliable, high-performance, enterprise-grade products. 

The ideal candidate will shape technical vision, lead cross-functional execution, productionize ML systems at scale, and establish best-in-class practices across the full machine learning lifecycle. 

Key Responsibilities 

Strategic Leadership & Innovation 

• Architect and execute Tekion’s strategic vision for Applied AI and Machine Learning, ensuring strong alignment with business objectives and industry needs. 
• Drive the R&D roadmap by identifying emerging technological opportunities and delivering scientifically grounded innovations. 
• Serve as the primary technical liaison between the R&D organization and executive leadership. 
• Contribute to the broader scientific community through publications and participation in leading academic conferences and journals.

Cross-Functional Delivery 

• Partner closely with Product, Engineering, Data, and Business teams to design and integrate advanced ML capabilities into core products and services. 
• Translate applied science prototypes (tabular ML, NLP/LLMs, recommendation systems, forecasting) into scalable production services. 
• Review, refactor, and optimize data science models for production readiness. 
• Mentor applied scientists and engineers, fostering a culture of technical excellence and innovation. 

ML Platform & Production Engineering 

• Build and operate robust CI/CD pipelines for machine learning systems. 
• Develop high-performance inference microservices (REST/gRPC) with schema versioning, structured outputs, and strict p95 latency targets. 
• Integrate with the LLM Gateway/MCP, including prompt and configuration versioning. 
• Design and implement batch and streaming data pipelines using technologies such as Airflow/Kubeflow, Spark/Flink, and Kafka. 
• Collaborate on enterprise system architecture with data engineers, platform teams, and architects. 

LLM & Agentic Systems Excellence 

• Implement advanced prompt management frameworks, including versioning, A/B testing, guardrails, and dynamic orchestration. 
• Monitor, detect, and mitigate risks unique to LLMs and agent-based systems. 
• Establish best practices for safe, reliable, and cost-efficient LLM deployment at scale. 

Lifecycle Management, Observability & Reliability 

• Own the end-to-end model and feature lifecycle, including feature store strategy, model/agent registry, versioning, and lineage. 
• Build deep observability across traces, logs, metrics, drift detection, model performance, safety signals, and cost tracking. 
• Ensure real-time service reliability through autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 
• Establish robust model evaluation frameworks and clearly quantify business impact for executive stakeholders. 
• Define and govern best practices across the full ML lifecycle while championing ethical and responsible AI. 

Developer Experience & Enablement 

• Create reusable templates, SDKs, CLIs, sandbox datasets, and documentation that make ML delivery fast, reliable, and repeatable across teams. 
• Drive platform standardization to make shipping ML the default path within the organization. 

 Core Competencies & Technical Expertise 
The successful candidate will demonstrate mastery in the following areas: 

Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI. 

Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data. 

Deep Learning: 

• Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch. 
• Extensive experience implementing advanced neural network architectures. 
• Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies. 

Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications. 

Technical Proficiencies: 

• Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous. 
• Proficiency with cloud computing platforms (GCP, Azure, AWS). 
• Expertise in experimental design (A/B testing, causal inference). 
• Proficient in version control systems (Git). 

Basic & Preferred Qualifications 

• Advanced degree (M.S. or Ph.D. preferred) in Computer Science, Statistics, Operations Research, Physics, or a related quantitative discipline. 
• 6+ years of post-academic experience in applied science, machine learning, or quantitative research roles, with a strong track record of translating complex models into measurable business impact. 
• Demonstrated success solving difficult, business-critical problems using rigorous, data-driven methodologies. 
• Proven hands-on experience in programming, large-scale data manipulation, and building production-grade models in real-world business environments. 
• Strong data visualization and executive communication skills, with the ability to translate complex analytical findings into clear, actionable insights for diverse stakeholders. 

LLM & Advanced AI Systems 

• Practical experience with LLMs, retrieval systems, vector databases, and graph/knowledge stores. 
• Hands-on experience with orchestration frameworks such as LangChain, LlamaIndex, OpenAI function calling, AgentKit, or similar ecosystems. 
• Solid understanding of modern agent architectures (reactive, planning, and retrieval-augmented agents) and safe execution patterns. 

Software Engineering & Distributed Systems 

• Strong software engineering fundamentals, including Python and at least one of Java, Go, or Scala. 
• Experience with API design, concurrency, testing strategies, and production code quality standards. 
• Proven experience building and operating microservices using REST/gRPC. 
• Hands-on experience with Docker, Kubernetes, and service mesh environments. 
• Strong performance and reliability engineering mindset. 

Data & Pipeline Engineering 

• Experience designing and operating batch and streaming pipelines using Airflow, Kubeflow, or similar orchestration tools. 
• Working knowledge of Spark or Flink for distributed data processing. 
• Experience with streaming platforms such as Kafka or Kinesis. 
• Strong grounding in data quality, validation, and governance practices. 

MLOps, Observability & Reliability 

• Experience with experiment tracking and model registries (e.g., MLflow), feature stores, A/B testing, shadow deployments, and drift detection. 
• Deep observability experience using tools such as OpenTelemetry, Prometheus, and Grafana. 
• Strong debugging skills for latency, tail performance, and memory/CPU bottlenecks. 

Cloud, Security & Compliance 

• Strong cloud experience, preferably AWS (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions, Lambda), including cost optimization practices. 
• Experience with secrets management, RBAC/ABAC, PII handling, and auditability requirements in production systems. 

 Ideal Candidate Profile 

• The ideal candidate is a technically exceptional Applied AI leader who combines deep scientific rigor with strong production engineering discipline. They have a proven ability to translate advanced machine learning and LLM research into scalable, reliable, and business-impacting systems. 
• This individual operates comfortably across the full spectrum—from research ideation and model development to platform architecture, production deployment, and real-time reliability. They bring strong ownership, systems thinking, and the ability to influence both technical teams and executive stakeholders 

 Perks and Benefits 

• Competitive compensation 
• Generous stock options 
• Medical Insurance coverage 
• Work with some of the brightest minds from Silicon Valley’s most dominant and successful companies