MLops Lead Engineer

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

Greetings!

Hiring For Large Product Based Company!

Role- Mlops Engineer

Experience- 8-12 years

Location- Pune, Nagpur

JD-

• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  

Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

Educational Qualification:• B.Tech / M. Tech / M.S. in Computer Science, Data Engineering, AI or related discipline.• Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, AzureDevOps Expert, GCP Professional ML Engineer) is highly desirable.• Contributions to MI-Ops or DevOps open source projects is preferred

Experience:• 7—10 years in machine learning operations or DevOps engineering.• Minimum 4 years building CVCD pipelines for AI/ML model deployment in enterprise orgovernment ecosystems.• Proven experience with containerized and microservice architectures.

Key Responsibilities:1.Design and manage continuous integration and delivery (CVCD) pipelines for AIIMLmodels across multiple environments.2. Establish model versioning, deployment, monitoring, and rollback mechanisms to ensurestability and traceability.3.Automate training, testing, and serving workflows using containerized solutions.4.Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.5. Collaborate with Data Science and Engineering teams to standardize model input/outputformats and performance metrics.6.Implement logging, monitoring, and alerting for deployed models to ensure highavailability and accuracy over time.7.Ensure compliance with Responsible Al guidelines for deployment, including bias auditingand explainability tracking.

• Technical Competencies:• MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP VertexAl Pipelines for end-to—end ML workflow orchestration• Containerization: Docker, Kubernetes, Helm charts, container registries, and microservicesarchitecture for ML workloads• CI/CD: Jenkins, GitLab CI, GitHub Actions, Azure DevOps with specialized ML pipelineintegration and automated testing• Infrastructure-as-Code: Terraform, CloudFormation, Ansible for reproducible MLinfrastructure provisioning and management• Cloud Platforms: AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, BlobStorage), GCP (GKE, Cloud Build, Cloud Storage)• Model Serving: TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.• Programming Languages: Python for automation, Bash scripting, YAML for configurationmanagement, basic understanding Of Go/Java• Database & Storage: Feature stores (Feast, Tecton), model registries, data versioning(DVC), and distributed storage systems• Workflow Orchestration: Apache Airflow, Prefect, Argo Workflows for complex MLpipeline scheduling and dependency management.

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).

Position Overview 

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems. 

Responsibilities 

• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms 
• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning 
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection 
• Deploy, maintain and monitor containerized ML workloads 
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows 
• Implement AI-driven data validation, schema and concept drift detection and metadata management. 
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability 
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention 
• Develop automated test suites for model performance, regression, edge cases and bias validation 
• Monitor model KPIs (accuracy, precision, recall, latency, calibration) 
• Ensure reproducability of experiments and production models 
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption 

Qualifications 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field 
• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering 
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure 
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn 

Kody Technolab Limited

AI/ML Lead (7+ Years Experience)

Location: Ahmedabad / Gandhinagar

Experience: 7+ Years

Employment Type: Full-Time

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy

cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have

strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and

cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy Machine Learning and Deep Learning models for classification,

regression, recommendation systems, NLP, Computer Vision, and Generative AI applications.

• Build and maintain end-to-end ML pipelines including data preprocessing, feature

engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related

frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama,

Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into

scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence,

Mathematics, or a related field.

• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and

Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment,

monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

• Knowledge of model interpretability, data annotation, and feature engineering.

• C++ experience for high-performance AI applications.

Why Join Kody Technolab Limited?

Opportunity to work on innovative AI products, Generative AI solutions, robotics integrations,

and enterprise-scale applications while collaborating with a highly skilled technology team.

Visit the Website to know more about us.

Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution

Kody Robots | Robotics Company in India for Autonomous Robots

About the role

You will be a pivotal member of the leadership team, responsible for driving the company's product and platform strategy while overseeing engineering excellence across AI, data, platform, DevOps and infrastructure. This is a mission-critical role for an engineering leader who is both strategic and hands-on, and who can build scalable technologies for global enterprise clients in a fast-paced innovation ecosystem.

Reports to: CEO · Location: Bengaluru, India — hybrid, 3 days a week in office

What you will do

Technology & product leadership

• Define and drive the technology vision, architecture and long-term platform roadmap.
• Oversee the architecture, design and delivery of highly scalable enterprise systems.
• Ensure engineering excellence, velocity and reliability across the product lifecycle.

Engineering & platform management

• Lead platform engineering, product technology, DevOps, infrastructure and Quality Engineering.
• Build robust cloud-native systems using the Azure, AWS and GCP ecosystems.
• Oversee operational effectiveness, including uptime, production reliability and cost optimisation.

Innovation & AI strategy

• Spearhead Mission AI by developing scalable, production-grade AI/ML and GenAI capabilities.
• Own the GenAI/LLM solutions architecture.
• Core specialisation in AI agents and autonomous workflows, data extraction and intelligent automation.
• Direct hands-on architectural oversight of large language models, applied AI and multi-layered deep learning products.
• Extensive expertise in building intelligent document automation systems — similar in complexity to patent parsing, legal workflow automation and structured decision intelligence tools.

Technical leadership

• A proven track record overseeing product architecture, tech strategy and cross-functional engineering execution across core full-stack and AI platforms.
• Collaborate with executive leadership on business strategy, client requirements and product delivery.
• Build, mentor and scale high-performing engineering teams with a growth mindset.
• Establish a strong technology culture grounded in ownership, innovation and continuous learning.

What success looks like

• Mission AI: build a world-class AI system leveraging GenAI, ML and enterprise-grade data engineering.
• Hyper-scaling: architect and scale the platform to match global industry leaders in the IP space.
• Culture building: develop a strong engineering organisation with high ownership, performance and innovation DNA.

Qualifications & experience

• Preferably under 15 years of enterprise software engineering experience across B2B SaaS or technology-first companies; fewer is fine for an exceptional engineer.
• A proven track record of taking early-stage AI/ML prototypes and scaling them into robust enterprise SaaS platforms featuring multi-agent orchestration, complex workflows and decision intelligence.
• Experienced in building and mentoring agile, lean startup teams of full-stack and machine learning engineers from the ground up.
• Proven leadership in defining and executing technology strategy and platform roadmaps.
• Extensive cloud-native engineering experience with Azure, AWS and GCP.

Technical expertise

• Strong full-stack engineering background (Java, Python, JavaScript frameworks).
• Expertise with JS frameworks such as React, Angular and Node.js.
• Experience building and scaling distributed systems and microservices.
• Strong knowledge of databases (SQL, NoSQL), data modelling and unstructured data management.
• Strong understanding of Agile methodologies and tools (Atlassian, Git, CI/CD pipelines).

Behavioural & leadership competencies

• Product and delivery management expertise, end to end, including delivery and customer support.
• Excellent communication, with the ability to influence executive stakeholders.
• High technical proficiency combined with strong business acumen.
• Strong analytical and decision-making skills.

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

Role Overview

We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.

Key Responsibilities

·       Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

·       GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

·       Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

·       MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

·       Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

·       Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

·       Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

·       Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

·       GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

·       Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

·       Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

·       Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

·       Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

·       Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

·       Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Role Summary 

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems—not solely conducting data analysis or research. It requires strong Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring. The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities. 

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps. 

Key Responsibilities 

Machine Learning Engineering 

• Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable. 
• Translate model requirements into robust, reusable, and testable Python components. 
• Build model training, validation, scoring, and inference workflows that can be integrated into production applications. 
• Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency. 
• Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior. 

Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate. 

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices. 

Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case. 

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data. 

Deep Learning & Model Architecture 

Data Platforms & ML Pipelines 

• Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications. 
• Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution. 
• Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference. 
• Use PySpark and distributed processing techniques where appropriate for large-scale workloads. 
• Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation. 

Productionization & MLOps 

• Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows. 
• Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices. 
• Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation. 
• Improve operational reliability, scalability, observability, and maintainability of ML systems. 
• Collaborate with platform, cloud, and DevOps teams on deployment automation and production support. 

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards. 

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations. 

Software Engineering & Technical Ownership 

• Develop clean, modular, documented, and maintainable code following engineering standards and design principles. 
• Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems. 
• Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support. 
• Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency. 
• Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities. 

Required Skills & Qualifications 

• Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality. 
• Strong ML foundation and mathematical understanding 
• Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring. 
• Strong SQL skills and experience working with structured data and data pipelines. 
• Hands-on experience with Snowflake for querying and preparing data for ML workflows. 
• Hands-on experience with Databricks for data processing and/or ML pipeline development. 
• Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate. 
• Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting. 
• Ability to design reliable, scalable, and maintainable software components and pipelines. 

Familiarity with GPU training concepts and distributed strategies such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), or equivalents is an advantage for large-model workloads. 

Understanding of model optimization, inference efficiency, and integration with production services or applications. 

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership. 

Preferred Skills 

• PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data. 
• MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management. 
• Experience integrating Snowflake and Databricks within enterprise ML workflows. 
• Cloud experience with AWS, Microsoft Azure, or Google Cloud. 
• REST APIs, containers, CI/CD, automated testing, logging, and observability. 
• Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications. 
• Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage. 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience. 
• At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth. 
• Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues. 

Title                                 : Senior AI Platform / MLOps Engineer

Experience                    : 6+ years

Work type                      : Chennai - Work from Office/other locations - Remote

Employment Type      : Full Time

Notice Period              : Immediate

Work Day                     :Mon to Fri

Key Responsibilities:

• Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control-plane topologies
• Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management
• Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression
• GitOps CI/CD, container registry, artifact/model versioning, environment promotion; observability and audit wiring (Splunk, Prometheus/Grafana)
• Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline
• Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

Technical Skills:

• 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred
• Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM — you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory-bandwidth trade-offs
• GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks
• Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

• Azure or AWS GPU compute operations experience

Strongly preferred

• NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards