T
Tekionvia Ashby
Engineering-Applied Science/Machine Learning/Data Science
IndiaPosted 3mo ago
ResearchMid LevelFull-time
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About the Role
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
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
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