Associate Director

Key Responsibilities
1. AI Infrastructure & Architecture: Design, build, and maintain scalable, reliable, and efficient infrastructure for training and deploying machine learning models at scale, including support for foundation models, LLM fine-tuning, and retrieval-augmented generation (RAG). Own end-to-end technical architecture, design reviews, and system design for AI platforms and services.
2. Team Leadership & Mentorship: Lead and mentor a team of AI/ML and software engineers, fostering a culture of engineering excellence, innovation, and collaboration. Guide the team in best practices for software development and MLOps, while remaining hands-on in code, design, and technical problem-solving.
3. MLOps & Automation: Own and drive the MLOps strategy. Implement and manage CI/CD pipelines for machine learning models, automating the entire lifecycle from data preparation to model monitoring and extending pipelines for LLM deployment and monitoring.
4. Production Deployment: Lead the technical efforts to integrate and deploy machine learning models into our production environments, ensuring high availability, low latency, and scalability, including deployment of LLM-powered applications and AI agents.
5. Cross-Functional Collaboration: Partner closely with data scientists, software engineers, architects, and product managers to understand model requirements and translate them into robust engineering solutions. Influence product and platform roadmaps through strong technical input and feasibility assessments.
6. Performance Optimization: Continuously monitor and optimize the performance of our AI systems, including model inference speed, resource utilization, and cost-effectiveness, with a focus on optimizing LLM inference efficiency.
7. Technology & Tooling: Evaluate and select the best tools, frameworks, and technologies for our AI engineering stack. Define and promote engineering standards, reference architectures, and reusable components for AI-driven solutions. Stay current with the latest advancements in the field.
8. Code Quality &Best Practices: Champion and enforce high standards for code quality, testing, security, reliability, and documentation within the AI engineering team.

Qualifications & Skills
. Educational Background: Bachelor's or Master's degree in Computer Science, Software
Engineering, or a related technical field.
. Professional Experience: 10+ years of professional software engineering experience, with at
least 3-4 years in a technical hands-on leadership role focused on AI/ML engineering or MLOps,
including applied experience with large language models (LLMs) and Generative AI in production.
Demonstrated track record of designing and delivering complex, distributed, production-grade AI
systems.
. GenAI & Agentic AI Expertise: Hands-on experience with frameworks for agentic AI (e.g.,
LangChain, LangGraph, CrewAI) and vector databases (e.g., FAISS, Pinecone, PostgreSQL, Azure
AI Search, etc.), Computer Vision, etc.
. Software Engineering Excellence: Deep expertise in a major programming language (e.g.,
Python, Java, Go) and a strong foundation in software architecture, data structures, and algorithms.
Experience with microservices, APIs, and high-throughput, low-latency systems is a plus.
. MLOps Expertise: Proven, hands-on experience building and managing MLOps pipelines using
tools like Kubernetes, Docker, Jenkins, MLflow, Kubeflow, or similar technologies.
. Cloud Proficiency: Extensive experience with at least one major cloud platform (AWS, GCP, or
Azure) and its AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).
. Leadership: Demonstrated ability to lead technical teams, mentor engineers, and manage
complex engineering projects from conception to completion, balancing strategic thinking with
hands-on technical execution.
. Problem-Solving: Strong analytical and problem-solving skills, with the ability to troubleshoot
complex issues in distributed systems. Ability to quickly understand business requirements, user
needs, and technical constraints. Skilled in converting ambiguous or high-level problems into
structured AI/ML solutions.
. Solution Design & Approach Evaluation:
Ability to propose multiple solution approaches to any AI/ML or GenAI problem. Strength in
evaluating pros, cons, feasibility, scalability, and risk associated with each approach. Expertise in
selecting the best-fit solution considering data availability, cost, time, and long-term maintainability.
Strong sense for when to use:
Traditional ML vs Deep Learning
Classical NLP vs LLMs
Fine-tuning vs RAG vs prompting strategies

Preferred Qualifications
. Experience with large-scale data processing technologies (e.g., Apache Spark, Kafka).
. Familiarity with infrastructure as code (IaC) tools like Terraform or CloudFormation.
. Experience optimizing deep learning models for inference (e.g., using TensorRT, ONNX) and
optimizing LLM inference for latency and cost efficiency.
. Contributions to open-source AI, GenAI or MLOps projects.