Senior Technical Lead

HCLTech; India; Presencial; INR 4.200.000 - 6.600.000; Jornada completa

Descripción de la vacante

HCL Technologies Limited is seeking a Senior Technical Lead to architect and deliver end-to-end AI/ML solutions in production environments. You will work on classification, forecasting, NLP, and GenAI initiatives, building scalable REST APIs and microservices on AWS.

You will lead design, experimentation, and validation of models, ensuring explainability and governance while guiding teams in best practices and delivery standards.

Formación

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Statistics, Engineering, or related field.
• 8-10 years of experience in AI/ML or Data Science roles.
• Strong Python programming and AWS experience.
• Experience building production-grade AI solutions and APIs.

Responsabilidades

• 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 and RAG systems.
• Develop AI copilots, intelligent assistants, and agentic workflows.
• Implement prompt engineering, evaluation frameworks, and guardrails.
• Develop production-grade AI services, APIs, SDKs, and microservices.
• Design scalable REST APIs and event-driven architectures.

Conocimientos

Python

AWS

SQL

Data Analysis

ML Algorithms

Deep Learning

NLP

LangChain

LLMs

REST APIs

Docker

Git

Spark

Vector Databases

Educación

Bachelor's/Master's in CS

Herramientas

TensorFlow/PyTorch

XGBoost/LightGBM

OpenSearch

Kafka

EKS

S3

API Gateway

LangGraph

Descripción del empleo

Career Opportunities: Senior Technical Lead (183948)

Requisition ID183948-Posted10/28/2026

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or related field.
• 8-10 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

• Good-to-Have Skills
• 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.