AI Lead Engineer
AGS Health · Bengaluru, Karnataka, India
Apply & track with Apply EdgeEXP : 7+ Yrs
This role requires deep expertise across traditional ML, Deep Learning, LLMs, RAG, Agentic AI, MLOps, cloud platforms, and production AI engineering.The individual will be responsible for translating complex business requirements into scalable AI solutions, making critical architecture and model-selection decisions, leading experimentation, driving solutions from POC to production, and mentoring AI/ML engineers.This is a hands-on technical leadership position. Candidates whose experience is primarily project/program management without significant AI engineering and architecture ownership may not be suitable.Key Responsibilities1. AI/ML & Deep Learning EngineeringDesign, develop and optimize advanced Machine Learning and Deep Learning solutions.Build production-grade AI applications using Python and modern software engineering practices.Apply strong knowledge of data structures, algorithms, software architecture, design patterns and clean architecture.Evaluate and select appropriate ML/DL algorithms and model architectures.Work extensively with Transformers, NLP, LLMs and Deep Learning architectures.Lead advanced model evaluation, error analysis and systematic model improvement.Establish robust ML experimentation, benchmarking and statistical evaluation frameworks.2. Generative AI & LLM EngineeringDesign and implement enterprise-grade Generative AI and LLM-based solutions.Implement RAG architectures, embeddings, semantic search and vector databases.Fine-tune and customize LLMs using techniques such as LoRA, PEFT and QLoRA.Design prompt engineering and LLM orchestration frameworks.Build Agentic AI architectures involving agents, tools and AI workflows.Establish LLM evaluation methodologies covering quality, accuracy, reliability and performance.Implement approaches for hallucination mitigation, guardrails and responsible model behavior.Evaluate foundation models and determine appropriate model selection based on business and technical requirements.3. AI Architecture & Production EngineeringArchitect scalable, secure and reliable end-to-end AI/ML platforms.Design AI services using microservices, REST APIs and FastAPI/Flask.Build distributed and asynchronous AI processing architectures.Design scalable model-serving and inference architectures.Optimize GPU/CPU utilization, inference latency and throughput.Apply optimization techniques including quantization, distillation, batching and caching.Design AI systems for high availability, fault tolerance and scalability.Lead architecture and technical design reviews.4. MLOps & Cloud EngineeringBuild and manage production AI/ML deployment pipelines.Work with Docker, Kubernetes and CI/CD pipelines.Implement ML lifecycle management using MLflow, Kubeflow or equivalent platforms.Design AI/ML solutions leveraging AWS SageMaker, Amazon Bedrock and other AWS AI services.Establish model versioning, deployment and rollback strategies.Implement model monitoring, drift detection and automated/controlled retraining strategies.Optimize cloud infrastructure for performance, scalability and cost.5. Data & AI Platform EngineeringWork with advanced SQL and large-scale datasets.Design scalable data-processing pipelines supporting AI/ML workloads.Implement feature stores and appropriate data/model version-control mechanisms.Establish data-quality and governance practices for production AI.Build robust experiment tracking and reproducibility processes.Technical Leadership ResponsibilitiesThe AI Lead Engineer will act as a technical leader for the AI engineering team and will be expected to:Define the technical roadmap and AI engineering strategy.Lead AI architecture and design reviews.Mentor AI/ML Engineers and Senior AI Engineers.Establish engineering, coding, experimentation and model-evaluation standards.Review technical designs, code, experiments and production architecture.Collaborate closely with Product, Engineering, Operations and Business teams.Translate business problems into technically feasible AI solutions.Drive AI initiatives from ideation/POC through production deployment and scaling.Make informed technical trade-offs across accuracy, latency, cost, scalability and maintainability.Create and maintain high-quality technical documentation.Communicate complex AI concepts and technical decisions effectively to technical and non-technical stakeholders.Mandatory Skills1. AI/ML Technical LeadershipStrong hands-on experience building AI/ML solutions.Advanced ML/DL algorithm and architecture expertise.Model experimentation, evaluation and optimization.Strong Python and production software engineering capabilities.Experience technically leading AI/ML Engineers.Mandatory2. AI / Generative AI ArchitectureStrong hands-on expertise in:Generative AILarge Language ModelsTransformersNLPRAGEmbeddingsVector databasesSemantic searchPrompt engineeringLLM orchestrationAgentic AIModel evaluation and guardrailsMandatory3. Production AI & MLOpsStrong experience taking AI/ML solutions from development into production, including:DockerKubernetesCI/CDMLflow/Kubeflow or equivalentModel servingModel monitoringVersioning and rollbackGPU/CPU optimizationScaling and production observabilityMandatory4. Solution Architecture & Business TranslationAbility to:Understand complex business problems.Translate requirements into AI architecture.Decide between traditional ML, DL, RAG, LLM fine-tuning, prompting and hybrid approaches.Design scalable enterprise AI solutions.Make architecture decisions considering accuracy, scalability, latency, maintainability and cost.Mandatory5. Technical Leadership & MentoringDemonstrated experience in:Architecture/design reviewsTechnical mentoringCoding and engineering standardsBuilding technical capability within AI teamsCross-functional stakeholder collaborationDriving AI POCs into productionMandatoryPreferred SkillsAWS AI/ML ecosystem experience.AWS SageMaker and Amazon Bedrock.Advanced GPU/inference optimization.LoRA, PEFT and QLoRA.Model quantization and distillation.Feature stores.Distributed AI/ML architectures.Large-scale data processing.Cloud infrastructure and AI cost optimization.Experience building enterprise-wide AI/GenAI platforms.Minimum Qualifications7+ years of experience in AI/ML, Data Science, Machine Learning Engineering or closely related AI engineering roles.At least 3+ years of strong hands-on experience building and deploying production AI/ML systems.Proven technical ownership of complex AI/ML or Generative AI solutions.Demonstrated experience taking AI solutions from experimentation/POC to enterprise production.Strong technical leadership and stakeholder-management capabilities.