Technical Lead

Job description

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or related field. 4-8 years of experience in AI/ML or Data Science roles. Strong Python programming and AWS experience. Key Responsibilities Build end-to-end ML solutions for classification, regression, recommendation, forecasting, clustering, anomaly detection, and optimization problems. Perform feature engineering, model experimentation, hyperparameter tuning, and model evaluation. Design and execute A/B testing and model validation strategies. Develop explainable and auditable ML models for business-critical applications. Monitor model performance and support retraining initiatives. Design and develop LLM-powered applications. Build RAG systems using vector databases and enterprise knowledge repositories. Develop AI copilots, intelligent assistants, and agentic workflows. Implement prompt engineering, prompt optimization, evaluation frameworks, and guardrails. Develop production-grade AI services, APIs, SDKs, and microservices. Design scalable REST APIs and event-driven architectures. Apply design patterns, SOLID principles, and clean coding standards. Work with Docker, EKS, SNS, SQS, and other AWS services. Key Responsibilities Must-Have Skills Python Strong hands-on experience with Python for developing production-grade AI and machine learning applications. Ability to write clean, maintainable, and reusable code following software engineering best practices. Experience developing APIs, automation frameworks, and AI services using Python. SQL & Data Analysis Strong proficiency in SQL for data extraction, transformation, and analysis. Experience working with large-scale structured and semi-structured datasets. Ability to perform exploratory data analysis and derive business insights from data. Machine Learning Strong expertise in machine learning algorithms and techniques including: Classification Regression Clustering Recommendation Systems Forecasting Anomaly Detection Hands-on experience with: Scikit-Learn XGBoost LightGBM CatBoost Deep understanding of: Feature Engineering Model Evaluation Hyperparameter Tuning Cross Validation Explainable AI Deep Learning & NLP Practical experience developing NLP solutions using TensorFlow or PyTorch. Understanding of transformers, embeddings, and modern NLP techniques. Experience with text classification, semantic search, summarization, information extraction, and conversational AI use cases. Generative AI Hands-on experience building enterprise-grade GenAI applications. Strong understanding of: Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Agentic AI Workflows Structured Output Generation Evaluation Frameworks Experience using frameworks such as: LangChain LangGraph API & Software Engineering Experience designing and developing RESTful APIs and microservices. Strong understanding of design patterns, object-oriented programming, and SOLID principles. Experience creating reusable AI components, SDKs, and shared libraries. AWS Cloud Technologies Working knowledge of AWS services commonly used for AI applications including: Amazon EKS SNS SQS Lambda S3 API Gateway Ability to build scalable, cloud-native AI solutions. Containerization Experience with Docker for packaging and deploying AI applications. Understanding of container-based application development and deployment. Version Control Strong experience with Git and collaborative development workflows including code reviews, branching strategies, and release management. Skill Requirements Apache Spark Experience processing large-scale datasets using Spark. Understanding of distributed data processing and big data workloads. AI Agent Frameworks Experience with CrewAI or similar multi-agent frameworks. Understanding of agent orchestration, tool usage, memory management, and autonomous workflows. Vector Databases Experience working with one or more of: OpenSearch pgvector Knowledge of embeddings, vector search, and semantic retrieval techniques. Knowledge Graphs & GraphRAG Understanding of knowledge graph concepts and graph-based retrieval techniques. Exposure to GraphRAG architectures for improving reasoning and explainability. Event-Driven Architecture Understanding of event-driven design patterns. Experience integrating SNS, SQS, Kafka, or similar messaging technologies into AI solutions." Other Requirements Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion. Disclaimer Privacy Statement Terms of use Sitemap Raise a Grievance We use cookies on our site. Please read more about cookies policy here. Opens in a new tab. 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