Senior AI/ML Engineer

About the Role
We are looking for a highly experienced Senior AI/ML Engineer who can lead the design, development, and delivery of production-grade AI systems. This role requires strong engineering judgment, hands-on experience implementing AI/ML solutions, expertise in model evaluation, and the ability to build safe, scalable, and maintainable AI workflows for enterprise use cases.
The Senior AI/ML Engineer will work closely with platform, product, application, and infrastructure teams to deliver reliable AI capabilities. The role will also involve mentoring junior and mid-level engineers, establishing engineering best practices, and making technical decisions that balance accuracy, cost, latency, security, and reliability.
Requirements
Programming and Engineering
Advanced proficiency in Python.
Strong experience with API design and integration.
Understanding of distributed systems and scalable application architecture.
Strong SQL and database fundamentals.
Experience with debugging, automated testing, and production troubleshooting.
Strong understanding of software engineering principles, code quality, and maintainability.
AI/ML and GenAI
6–10+ years of relevant software engineering and/or AI/ML experience.
Hands-on experience building and supporting production AI/ML or GenAI systems.
Strong understanding of model inference and model behavior analysis.
Experience with prompt engineering and structured AI outputs.
Experience designing and implementing AI evaluation workflows.
Ability to evaluate models based on measurable quality, performance, cost, and reliability metrics.
Architecture and Production Engineering
Experience designing AI workflows, service integrations, and data pipelines.
Understanding of queues, asynchronous processing, monitoring, and operational reliability.
Experience integrating AI services and models into production applications.
Ability to design scalable and maintainable AI services and workflows.
Strong understanding of API contracts, data validation, error handling, and system reliability.
Leadership
Proven experience mentoring or technically guiding junior and mid-level engineers.
Strong technical decision-making and problem-solving skills.
Ability to communicate complex technical concepts clearly to technical and non-technical stakeholders.
Experience documenting architecture, engineering standards, and technical decisions.
Ability to collaborate effectively across Product, Engineering, Platform, Infrastructure, QA, and Security teams.
Responsibilities
Technical Leadership
Lead the design and implementation of AI-powered features and model-integration patterns.
Define engineering standards for prompts, schemas, model evaluation, monitoring, and AI workflows.
Mentor Junior and Mid-Level AI/ML Engineers and provide technical guidance.
Make pragmatic trade-offs between model accuracy, cost, latency, scalability, and safety.
Review technical designs and provide recommendations for production AI architecture.
Establish reusable engineering patterns for AI application development.
Production AI Systems
Design and implement reliable AI workflows with validation, fallback, and human-review mechanisms.
Evaluate and select AI models and services based on measurable performance, quality, latency, cost, and reliability criteria.
Build, review, and improve APIs for model inference, extraction, validation, and analytics.
Design workflows for model routing based on workload, complexity, cost, and performance requirements.
Improve the reliability and maintainability of production AI systems.
Support troubleshooting and resolution of AI-related production issues.
Model Evaluation and Optimization
Design evaluation datasets and methodologies for AI/ML and GenAI systems.
Establish measurable criteria for model accuracy, consistency, latency, cost, and reliability.
Analyze model failures, hallucinations, edge cases, and recurring error patterns.
Establish regression testing and release gates for AI models and workflows.
Develop feedback loops that continuously improve model performance and output quality.
Identify opportunities for model, prompt, workflow, and infrastructure optimization.
Governance, Safety, and Operations
Define responsible AI standards for model usage, validation, monitoring, and release readiness.
Design guardrail strategies covering PII handling, prompt injection, hallucination mitigation, and human oversight.
Ensure model outputs are appropriately bounded, schema-validated, and auditable before they can trigger business actions.
Establish model, prompt, dataset, and version governance practices.
Drive tenant-isolation strategies across data, logs, artifacts, and processing results.
Establish monitoring for quality drift, recurring failure modes, unsafe outputs, latency, and cost anomalies.
Review data de-identification, redaction, retention, and access-control practices.
Create and maintain incident-response procedures for AI failures, privacy issues, incorrect outputs, and automation errors.

Work arrangement
No