AI Tech Lead

Role Overview: The Technical Lead for Generative AI & ML will serve as the critical bridge between the CTO and the engineering team. This is not a managerial role in the traditional sense — it is a deeply technical, research-driven leadership position where you will own the technical direction of the team, unblock engineers, resolve complex architectural and algorithmic problems, and translate the CTO's vision into executable engineering roadmaps.

You will be expected to stay ahead of the curve in Generative AI research, proactively identify opportunities, and drive initiatives from ideation to production. If you are someone who reads research papers on weekends, enjoys debugging deep learning issues at midnight, and loves mentoring engineers to unlock their potential — this role is built for you.

Key Responsibilities: Technical Leadership & Bridge Role

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Act as the primary technical liaison between the CTO and the engineering team, ensuring seamless translation of strategic goals into detailed technical plans and sprints.

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Communicate upward with the CTO on architecture decisions, research findings, blockers, and team progress with clarity and precision.

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Actively participate in product and strategy discussions, providing ground-level technical insights to influence roadmap decisions.

Hands-On Engineering

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Write, review, and own production-grade code in Python, ensuring high code quality, test coverage, and performance.

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Lead the design and development of Generative AI systems including LLM fine-tuning, RAG pipelines, agentic frameworks, multimodal models, and inference optimization.

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Build and maintain scalable ML infrastructure for training, evaluation, and deployment of large-scale models.

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Conduct thorough code reviews that go beyond syntax — evaluate architectural soundness, scalability, and maintainability.

Research & Innovation

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Proactively track and evaluate the latest Generative AI and ML research papers, identifying opportunities to apply novel techniques to KenexAI's product.

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Initiate and lead internal research experiments, proof-of-concepts, and benchmarking studies.

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Contribute to KenexAI's research culture through knowledge-sharing sessions, internal tech talks, and written documentation.

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Drive innovation by proposing new approaches, tools, and architectures before being asked — a self-starter who does not wait for directions to explore better solutions.

Team Mentorship & Enablement

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Be the go-to person for resolving technical doubts, debugging complex issues, and unblocking team members promptly.

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Mentor junior and mid-level engineers through pair programming, design reviews, and structured feedback.

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Foster a culture of technical excellence, curiosity, and psychological safety within the team.

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Identify skill gaps in the team and proactively design learning paths or workshops to address them.

Architecture & System Design

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Lead end-to-end system design of AI/ML pipelines — from data ingestion and model training to API deployment and monitoring.

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Define and enforce best practices for MLOps, model versioning, experiment tracking, and reproducibility.

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Evaluate and introduce new tools, frameworks, and cloud services that improve team productivity and system performance.

Required Skills & Qualifications:

Must-Have Technical Skills

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Deep expertise in Python with strong fundamentals in data structures, algorithms, and software engineering principles.

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Hands-on experience with LLMs (GPT, LLaMA, Mistral, Gemini, Claude, etc.) including fine-tuning, RLHF, PEFT, LoRA, and QLoRA.

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Strong command of Retrieval-Augmented Generation (RAG) architectures, vector databases (Pinecone, Weaviate, Qdrant, FAISS), and embedding models.

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Proficiency in ML frameworks: PyTorch (required), TensorFlow / JAX (preferred).

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Experience building and deploying AI-powered APIs and microservices using FastAPI / Flask.

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Familiarity with Agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI.

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Working knowledge of cloud platforms (AWS / GCP / Azure / Databricks) and container orchestration (Docker, Kubernetes).

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Experience with MLOps tools: MLflow, Weights & Biases, DVC, or similar for experiment tracking and model lifecycle management.

Research & Innovation Traits

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Demonstrated ability to read, understand, and implement concepts from cutting-edge ML/AI research papers.

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Strong intuition for what works in AI systems versus what sounds good on paper.

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History of proactively driving technical initiatives without being directed.

Leadership & Communication

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Proven experience leading a technical team of 3 or more engineers in an AI/ML environment.

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Ability to explain complex technical concepts clearly to both technical and non-technical stakeholders.

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Empathetic communicator who can deliver constructive feedback and inspire team confidence.

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Highly self-motivated with a strong sense of ownership and accountability.

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Knowledge of advanced ML concepts, including deep learning, NLP, recommendation systems, or generative models.

Good to Have:

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Experience with multimodal AI systems (vision-language models, text-to-image, speech models).

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Publications, open-source contributions, or conference talks in AI/ML.

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Experience with edge AI deployment, model compression, quantization, and distillation.

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Familiarity with evaluation frameworks for LLMs (RAGAS, HELM, EleutherAI LM Eval Harness).

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