L&T Off Campus Drive 2026 – Multiple Roles | Apply Online

• Bachelor of Technology (BTech) & Master of Technology (MTech)
• Required Experience: 0 – 2 Years

Skills:

• Machine Learning
• Computer vision
• Artificial Intelligence

Roles and Responsibilities:

Machine Learning & Artificial Intelligence

• Assist in collecting, cleansing, preprocessing, and validating structured and unstructured datasets.
• Support the design, development, training, and evaluation of machine learning and deep learning models.
• Perform feature engineering, model tuning, and performance optimization activities.
• Conduct model validation, testing, benchmarking, and documentation.
• Assist in deploying and monitoring AI/ML models in development and production environments.
• Analyze model performance and recommend improvements for accuracy and efficiency.

Generative AI Development

• Assist in developing applications utilizing Large Language Models (LLMs) and multimodal AI models.
• Support prompt engineering, prompt optimization, and response evaluation activities.
• Participate in Retrieval-Augmented Generation (RAG), fine-tuning, and model customization projects.
• Develop and test AI-powered chatbots, virtual assistants, and content generation solutions.
• Evaluate model outputs for quality, factual accuracy, safety, and compliance with Responsible AI practices.
• Integrate Generative AI capabilities into enterprise applications through APIs and frameworks.

Computer Vision Engineering

• Assist in developing image and video analytics solutions using computer vision techniques.
• Support implementation of object detection, image classification, segmentation, tracking, and OCR models.
• Work with image and video datasets for annotation, labeling, augmentation, and preprocessing.
• Assist in training and evaluating deep learning models using frameworks such as OpenCV, TensorFlow, PyTorch, and YOLO.
• Support deployment and optimization of vision models for edge devices, cloud platforms, and real-time applications.
• Perform quality assessments and accuracy analysis of computer vision systems.

Data Analytics & Engineering

• Conduct Exploratory Data Analysis (EDA) and generate insights from large datasets.
• Create visualizations, reports, and dashboards to communicate findings effectively.
• Support development of data pipelines and workflows for AI applications.
• Ensure data quality, integrity, and governance standards are maintained.
• Assist in managing and processing image, text, audio, and video datasets.

AI Solution Development & Integration

• Collaborate in building intelligent automation and AI-driven business solutions.
• Integrate AI, Generative AI, and Computer Vision models with web, mobile, and enterprise applications.
• Assist in developing APIs, microservices, and cloud-based AI solutions.
• Participate in software testing, debugging, and troubleshooting activities.
• Follow coding standards, version control, and software development best practices.

Research & Innovation

• Stay updated on advancements in AI, Machine Learning, Generative AI, Computer Vision, and Deep Learning.
• Conduct proof-of-concept (PoC) development for emerging technologies and use cases.
• Evaluate new AI frameworks, tools, and platforms for potential adoption.
• Contribute innovative ideas for enhancing AI products, services, and operational processes.
• Participate in technical discussions, hackathons, and innovation initiatives.

Documentation & Compliance

• Prepare technical documentation, model documentation, and project reports.
• Document datasets, training procedures, model evaluations, and deployment processes.
• Follow Responsible AI, cybersecurity, data privacy, and ethical AI guidelines.
• Ensure compliance with organizational, industry, and regulatory standards.

Collaboration & Learning

• Work closely with AI Engineers, Data Scientists, Software Developers, Product Teams, and Business Stakeholders.
• Participate in Agile ceremonies, code reviews, and team meetings.
• Continuously enhance technical knowledge through training, mentoring, and self-learning.
• Demonstrate a proactive attitude toward learning and adopting emerging technologies.

Relevant Experience:

• 1–3 years in an IT Infrastructure Operations, NOC, Service Desk, or Data Center Operations environment.
• 24×7 support operations, enterprise monitoring tools, ITSM platforms (ManageEngine, ServiceNow, BMC Remedy, Jira), and basic cloud or virtualization environments.
• 1–3 years of hands-on experience in Infrastructure Monitoring, Incident Management, Windows/Linux Administration, Active Directory, Virtualization (VMware/Hyper-V), Kubernetes, Storage & Backup Monitoring, Network Diagnostics, and ITSM tools (e.g., ServiceNow).
• 1–3 years of experience in Hardware Support, Documentation, ITIL/SOP adherence, Shift Operations, Asset & Vendor Coordination, Customer Support, and Operational Reporting

Roles & Responsibilities:

Infrastructure Monitoring

• Continuously monitor the health, availability, and performance of servers, virtualization, Kubernetes, storage, backup, network, and GPU infrastructure using enterprise monitoring tools.
• Detect alerts, perform initial health checks, validate service availability, and initiate incident notifications as per operational procedures.

Incident Management

• Log, categorize, prioritize, and troubleshoot Level 1 incidents using SOPs and knowledge articles while ensuring SLA compliance.
• Restore services where possible and escalate unresolved or critical incidents to L2/L3 teams with complete documentation.

Windows & Active Directory Support

• Perform user account administration, password resets, account unlocks, and basic Windows server health verification.
• Validate Windows services and escalate server or Active Directory issues requiring advanced administration.

Linux Operations Support

• Monitor Linux server availability, service status, and system logs to identify operational issues.
• Execute approved service restarts and escalate operating system issues beyond standard support procedures.

Virtualization Support

• Monitor virtual machine availability, health, and resource utilization across virtualization platforms.
• Perform basic VM recovery activities and escalate hypervisor or platform-related issues to specialized teams.

Kubernetes Operations Support

• Monitor Kubernetes clusters, nodes, pods, and services to ensure platform availability.
• Perform approved pod/service restarts and escalate cluster or orchestration-related issues.

Storage & Backup Support

• Monitor storage health, capacity, and backup job execution to ensure operational continuity.
• Perform authorized backup recovery actions and escalate storage or backup infrastructure failures.

Network & Infrastructure Diagnostics

• Perform basic network diagnostics, connectivity verification, and infrastructure service validation.
• Monitor network health and escalate complex connectivity or performance issues to network support teams.

GPU Infrastructure Support

• Monitor GPU server health, utilization, and hardware alerts to maintain operational readiness.
• Coordinate hardware replacement activities and vendor support for GPU-related incidents.

Hardware Support

• Perform basic hardware health checks and assist in diagnosing infrastructure component failures.
• Support hardware replacement activities, post-maintenance validation, and asset record updates.

Documentation & Reporting

• Maintain accurate incident records, operational logs, and shift handover documentation.
• Follow SOPs, contribute to knowledge management, and report recurring issues for continuous service improvement.

Job Purpose: The Frontline Infrastructure Support Engineer (L1) serves as the first point of contact for infrastructure-related incidents and service requests. The role is responsible for continuous monitoring of enterprise IT infrastructure, performing initial diagnostics, executing predefined operational tasks, restoring services using standard operating procedures (SOPs), and escalating unresolved issues to L2/L3 teams while ensuring SLA compliance.

Eligibility:

• Freshers with strong academic grounding in AI/ML are highly encouraged
• Experienced engineers (2+ years) in AI/ML roles are welcome
• Must come from Computer Science or AI/ML specialization

Note:  This role is ideal for freshers with strong AI fundamentals or engineers with up to 2+ years experience in AI/ML, data science, or Azure AI services.

Technical Skills Required:

AI/ML Fundamentals (Mandatory)

• Strong understanding of ML algorithms, deep learning, transformers, CNNs, RNNs.
• Experience with Python, PyTorch/TensorFlow.
• Knowledge of prompt engineering, embeddings, LLM behavior.

Azure Ecosystem Skills

• Azure OpenAI / Azure AI Foundry
• Azure Cognitive Services (Vision, Document Intelligence, Search)
• Azure ML, Azure Functions, Logic Apps
• Azure Blob Storage, Azure Databricks (added advantage)

Computer Vision Skills

• YOLOv8, Detectron2, OpenCV
• OCR engines – Azure OCR / Form Recognizer
• Video processing with Python/FFmpeg

NLP & Document AI

• LLM fine-tuning
• RAG (Retrieval Augmented Generation)
• Metadata extraction and reconciliation
• Clause extraction, compliance validation (added advantage)

Software Skills

• Python is mandatory
• Good understanding of DevOps, APIs and integration workflows
• Basic knowledge of SQL/NoSQL

Soft Skills

• Problem-solving mindset with strong analytical thinking
• Ability to understand complex engineering workflows
• Good communication and documentation skills
• Ability to work with cross-functional engineering teams

Key Responsibilities:

1. Azure AI & Chatbot Development

• Build AI-powered chatbots using Azure OpenAI, Azure Bot Service, Prompt Flow, and Cognitive Search.
• Develop conversational workflows for engineering, operations, and project management teams.
• Integrate chatbots with Teams, SharePoint, Azure Logic Apps, and engineering data for internal use cases.
• Fine-tune LLMs (GPT?4/40, Phi?3, etc.) using domain-specific datasets for engineering and EPC tasks.

2. Video Analytics for Safety & Operational Monitoring

• Develop and deploy deep learning–based video analytics models for:
  • PPE detection
  • Safety violations
  • Site hazard identification
  • Activity tracking
• Implement vision models (YOLOv8/Detectron2) for object detection, tracking, and anomaly detection.
• Integrate video pipelines into the Azure ecosystem (Blob → Functions → AI Models → Dashboards).

3. AI/ML for P&ID Processing & MTO Extraction

• Build systems for automatic extraction of MTO (Material Take-Off)from P&IDs using:
  • Computer vision
  • OCR (Azure Document Intelligence / Form Recognizer)
  • Graph-based engineering entity mapping
  • NLP & LLM-based reasoning for engineering validation
• Contribute to AI agent workflows for cross-verifying P&IDs, 3D models, line lists, and engineering documents.
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4. Engineering Document Automation (EPC Use Cases)

• Build models for drawing analysis, tolerance checks, metadata reconciliation, and compliance validation.
• Work with Azure AI Foundry to fine-tune models for Engineering Drawing Audit Automation.
• Support development of autonomous/agentic AI systems for EPC workflows.

About the Role:

• L&T Energy Offshore is accelerating its digital transformation by deploying advanced AI/ML solutions across Engineering, Procurement, Construction, Safety, and Compliance. We are looking for an enthusiastic AI/ML Engineer who can build, deploy, and optimize intelligent systems such as chatbots, video analytics pipelines, and engineering document automation tools (including MTO extraction from P&IDs).