Technical Lead - AI (Artificial Intelligence)
Posted Updated
Description
Position - Technical Lead/Senior Technical Lead/Architect
Department - AI/ML
Experience - 6 - 12 Years
Role: Lead the architecture and delivery of scalable AI solutions across machine learning, Generative AI, and agentic systems.
Skills Qualifications:
- Education: B.Tech is mandatory. Candidates must hold a B.Tech/M.Tech/Master s degree in Computer Science, Statistics, Artificial Intelligence, or a relevant engineering discipline from a Tier 1 or Tier 2 college.
- Experience: 6-12 years of relevant experience in Machine Learning, Artificial Intelligence (AI), or Data Science roles.
- Academic Preference: Strong academic background with a degree from a reputed Tier 1 or Tier 2 institution.
Key Responsibilities:
- Translate business requirements into AI architecture, technical roadmaps, and implementation plans.
- Define model selection, RAG, fine-tuning, and deployment strategies for LLMs and SLMs.
- Design secure integrations with enterprise applications and data platforms.
- Own architecture reviews, engineering standards, and trade-offs across quality, latency, and cost.
- Guide engineering teams from proof of concept to production.
Required Skills:
- 6-12 years in software/data engineering and AI/ML, with experience owning solution architecture.
- Strong Python, software design patterns, APIs, microservices, and distributed systems knowledge.
- Strong foundation in classical ML, deep learning, model evaluation, and explainability.
- Strong understanding of Transformer architecture, LLMs/SLMs, embeddings, RAG, and agentic workflows.
- Hands-on experience with fine-tuning, LoRA/QLoRA, PyTorch, and Hugging Face.
- Experience designing data pipelines, vector search, knowledge retrieval, and enterprise data integration.
- Experience with GPU sizing, model serving, containers, and cloud architecture-Azure preferred.
- Knowledge of MLOps/LLMOps, CI/CD, model versioning, evaluation, monitoring, and rollback strategies.
- Ability to design for scalability, availability, disaster recovery, and performance.
- Knowledge of AI security, access control, data privacy, prompt injection defenses, and responsible AI.
- Ability to assess build-versus-buy options, platform choices, and infrastructure costs.
- Strong stakeholder communication, architecture documentation, technical leadership, and mentoring skills.
Nice to Have
- Model compression, quantization, knowledge distillation, and Transformer pruning.
- Private/on-premises, hybrid-cloud, or multi-tenant AI deployments.
- Multi-agent orchestration, tool integration, and human-in-the-loop workflows.
- Distributed training, inference optimization, and Kubernetes-based GPU workloads.
- Knowledge graphs, GraphRAG, multimodal AI, or domain-specific model adaptation.
- Infrastructure as Code and cloud solution architecture certifications.
- Experience supporting technical proposals, solution estimates, and client architecture workshops.
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- Agentic AI
- AI
- API
- Azure
- CI/CD
- Cloud
- Data Engineering
- Data Pipelines
- Data Science
- Deep Learning
- Design Patterns
- Distributed Systems
- Distributed Training
- Embeddings
- Fine Tuning
- Generative AI
- GPU
- Hugging Face
- Infrastructure as Code
- Kubernetes
- LLM
- LLMOps
- Machine Learning
- Microservices
- MLOps
- Model Evaluation
- Proof of Concept
- Python
- PyTorch
- Quantization
- Statistics
- Vector Search