Technical Lead - Generative AI
First seen by Alion on Oct 7, 2026.
Job Summary:
We are looking for an experienced Technical Lead Generative AI to design, build, and scale enterprise-grade AI systems spanning Generative AI, Machine Learning, and Deep Learning.
In this role, you will own an end-to-end AI solution architecture, from business problem framing and hypothesis formulation to model development, deployment, and production monitoring. You will work closely with cross-functional teams and clients to deliver secure, scalable, cost-efficient, and responsible AI solutions, while providing strong technical leadership and mentoring within the GenAI ecosystem.
Responsibilities:
- Lead end-to-end AI solution development, from ideation and experimentation to production deployment and monitoring, ensuring measurable business impact.
- Architect and deliver Generative AI solutions, including LLM-powered applications, RAG pipelines, embeddings, prompt engineering, and agent-based workflows.
- Design and productionize Machine Learning and Deep Learning models, covering supervised, unsupervised, and deep learning use cases (NLP and/or Computer Vision) and recommendation engines.
- Build hybrid AI systems combining GenAI with classical ML/DL models to solve complex, real-world enterprise problems.
- Implement MLOps and LLMOps practices, including model versioning, automated testing, drift detection (data, concept, and prompt drift), and performance monitoring.
- Collaborate closely with Data Engineers and Analysts to ensure high-quality data pipelines, feature readiness, and scalable data architectures.
- Maintain high standards of code quality, CI/CD practices, documentation, and architectural governance for AI systems.
- Engage directly with stakeholders to translate business requirements into technical architectures, validate hypotheses, and guide decision-making.
- Champion rapid prototyping and lean experimentation, enabling fast validation before large-scale investments.
- Provide technical leadership and mentorship to junior engineers and data scientists, fostering a culture of excellence and continuous learning.
- Contribute to internal innovation through POCs, accelerators, blogs, workshops, and technical demos.
Requirements:
- 7 - 12 years of overall experience, with 8+ years in Data Science / ML system development.
- Strong experience in solution architecture and full lifecycle delivery of AI/ML/GenAI systems.
- Hands-on expertise with LLMs, RAG, embeddings, prompt engineering, and agent frameworks (e.g., LangChain, CrewAI, Bedrock Agents, OpenAI SDK).
- Solid experience with Machine Learning algorithms (regression, tree-based models, clustering, anomaly detection) and Deep Learning architectures for NLP and/or Computer Vision.
- Experience with model training and fine-tuning, including LoRA/QLoRA and transfer learning techniques.
- Strong proficiency in Python and modern ML/DL libraries (PyTorch, TensorFlow, Hugging Face, Scikit-learn, Pandas, NumPy).
- Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or ChromaDB.
- Practical experience with MLOps / LLMOps, CI/CD pipelines, containerization, and cloud-based AI deployments.
- Hands-on experience deploying AI solutions on AWS (preferred); exposure to Azure OpenAI or Vertex AI is a plus.
- Strong understanding of data privacy, security best practices, and responsible AI principles.
What sets you apart:
- Contributions to open-source GenAI / ML frameworks or active participation in the AI community.
- A strong portfolio of AI accelerators, internal platforms, or client-facing GenAI initiatives.
- Exposure to frontend technologies (React, Streamlit, Gradio) for end-to-end AI product ownership.
- Experience across multiple cloud GenAI ecosystems (AWS, Azure, GCP).
- Deep familiarity with enterprise-grade MLOps and LLMOps tooling and practices.
- Prior experience driving AI-led revenue growth, operational efficiency, or large-scale client adoption.
Skills
Generative AI, Machine Learning, Python, LLM, LangChain, Deep Learning, MLOps, Prompt Engineering, PyTorch, VectorDB