Job Title: Senior AI Engineer – ML & Generative AI
Role Overview
We are seeking a
hands-on Senior AI Engineer
with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying
LLM- and GenAI-driven systems . This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes.
You will work in
small, high-impact delivery teams (2–3 engineers per initiative)
and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.
Key Responsibilities
AI Solution Design & Problem Solving
Partner with business and product stakeholders to translate real-world problems into practical AI solutions.
Determine when to apply:
Traditional ML approaches (classification, regression, clustering, recommendation systems)
LLM / GenAI approaches, including agentic workflows
Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.
Design iterative AI workflows and propose alternative solution approaches where applicable.
Hands-on Engineering & Delivery (70–75%)
Build and own end-to-end AI systems, including:
Data ingestion and processing pipelines
Feature engineering and prompt construction
ML and LLM integration and orchestration
API-based AI services for downstream consumption
Deploy and harden production AI systems with:
Error handling and fallback mechanisms
Guardrails, safety controls, and exception handling
Observability (logging, metrics, tracing, dashboards)
Ensure production readiness through:
Performance tuning and latency optimization
Cost management and optimization strategies
Scalability and reliability planning
Implement AI system controls such as:
Input validation and prompt injection mitigation
Configurable policies and kill switches
Transition PoCs into production-grade systems through refactoring, testing, and system hardening.
ML & Generative AI Expertise
Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
Design and implement
RAG (Retrieval-Augmented Generation)
architectures.
Apply prompt engineering, evaluation techniques, and iterative optimization.
Build and evolve tool-based and agentic workflows, including multi-agent systems.
Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).
Collaboration & Technical Leadership (25–30%)
Act as a senior technical contributor within small delivery teams.
Debug complex AI system behavior and production issues beyond prompt-level tuning.
Contribute to architectural and design decisions alongside architects and platform teams.
Collaborate closely with:
Product managers and business stakeholders
Platform, cloud, and infrastructure teams
Uphold strong software engineering practices and delivery discipline.
Required Skills & Experience
Software & Systems Engineering
10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
Strong backend development skills (Python, Java, Node.js, or similar languages).
Experience designing and building REST or gRPC-based services.
Solid understanding of distributed system design.
Containerization and orchestration experience (Docker, Kubernetes).
AI / ML
Hands-on experience across traditional ML and modern GenAI systems.
Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.
Experience building or deploying:
ML-driven production systems
LLM-based applications
Ability to select ML vs. LLM-driven approaches based on business and operational constraints.
Cloud & DevOps
Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
Experience with CI/CD pipelines and deployment automation.
Understanding of model, code, and configuration versioning best practices.
Observability & Production Readiness
Experience implementing logging, monitoring, and tracing for production systems.
Familiarity with system resilience patterns such as:
Rate limiting
Failover strategies
Kill-switch mechanisms
Problem Solving & Mindset
Strong ability to solve ambiguous, real-world engineering problems.
Comfortable working in fast-moving, iterative environments.
Ownership mindset with a bias toward practical, scalable solutions.
Communication & Collaboration
Experience working in cross-functional teams.
Ability to clearly articulate technical and business trade-offs, including:
LLM vs traditional ML
Build vs buy decisions
Speed vs robustness
Good to Have
Experience with enterprise AI platforms or internal AI frameworks.
Prior production experience with:
Agentic architectures
Multi-agent systems
RAG-based systems at scale
Exposure to AI governance, safety, and compliance considerations.
Experience mentoring junior engineers or owning technical modules.
Hands-on experience optimizing performance and cost for AI workloads.