Engineering-Applied Science/Machine Learning/Data Science Professional

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
T he 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