Key Responsibilities: •Develop computer vision solutions for machine learning applications. •Design deep learning models using frameworks like PyTorch, TensorFlow, and OpenCV. •Implement algorithms for object detection, segmentation, classification, and tracking. •Work with large datasets for image processing and develop scalable models. •Deploy models on edge devices to optimize inference efficiency and resource utilization. •Collaborate with data scientists to understand project requirements and deliver solutions. •Implement and evaluate model performance to ensure accuracy and efficiency. •Stay up to date with the latest techniques in deep learning and classical computer vision Qualifications: •Bachelor s degree in Computer Science, or related field. •3+ years of experience in computer vision. •Strong skills in Python and experience with deep learning frameworks like PyTorch and TensorFlow. •Experience with computer vision libraries such as OpenCV. •Proficiency in classical computer vision techniques, including SIFT, SURF, feature extraction, image manipulation, and calibration. •Experience in deploying models to edge devices for real-time computer vision applications. •Strong understanding of Data Structures and Algorithms (DSA) with hands-on coding experience. •Strong experience working with large-scale datasets. •Excellent communication and collaboration skills.

Technical Skills & Experience: Programming Skills: Proficiency in Python, capable of developing AI-driven test scripts. Test Automation: Solid experience in test automation practices. Model Evaluation: Hands-on experience evaluating and testing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and computer vision models. Evaluation Frameworks: Skilled in designing evaluation frameworks to assess AI model performance, accuracy, bias, and safety. Familiarity with open-source evaluation tools such as DeepEval and similar frameworks. Prompt Engineering & Metrics: Knowledge of prompt engineering and LLM evaluation metrics like perplexity, BLEU, ROUGE, and human evaluation techniques. RAG System Testing: Experience in testing RAG systems, including retrieval quality assessment and hallucination detection. Computer Vision Testing: Familiarity with vision model testing methodologies and image processing evaluation. CI/CD Integration: Working knowledge of Continuous Integration/Continuous Deployment (CI/CD) tools like GitLab, Jenkins, or Azure DevOps. Preferred Qualifications Evaluation Framework Proficiency: Hands-on experience with at least one open-source AI evaluation framework, such as DeepEval, is essential. Vector Databases & Embedding Evaluation: Familiarity with vector databases like FAISS or Milvus, and experience in evaluating embedding models for tasks such as similarity search and retrieval. Multimodal AI Systems: Understanding of AI systems that process multiple data modalities (e.g., text, images) and the unique challenges involved in their evaluation. Human-in-the-Loop Evaluation: Experience incorporating human feedback into AI evaluation processes to enhance model performance and reliability Perks and benefits Best of Technology Flexible Working Hours Rewards and Recognition Medical Insurance Round the Clock Snacks Paid Time-Off and Vacation

Responsibilities Experience in a SRE, DevOps, or Systems Engineering role Collaborate with cross-functional teams on CI/CD pipelines using Jenkins & Git. Proficient in monitoring/logging tools (Prometheus, Grafana, etc.) Health insurance Provident fund Flexi working Free meal

What Youll Do: Build and maintain robust, scalable ETL/ELT pipelines for large-scale, high-volume, and multi-modal datasets. Architect and manage data lakes, warehouses, and marts for structured and unstructured data. Collaborate with ML, NLP, and Speech teams to deliver high-quality, ML-ready datasets. Implement data quality checks, lineage tracking, versioning, and compliance controls. Optimize pipeline performance and handle infrastructure reliability and observability (logging, alerting, monitoring). Develop internal tools and workflows to automate ingestion, labeling, and transformation processes. Mentor junior engineers and help establish best practices in data engineering and MLOps. What We’re Looking For: 4+ years of experience in data engineering or backend systems for ML/AI workflows. Advanced SQL skills and experience with analytical databases (PostgreSQL, BigQuery, Redshift, Snowflake). Strong Python or Scala skills for data transformation and pipeline orchestration. Proficiency with workflow tools like Airflow, Prefect, or DBT. Experience with cloud platforms (AWS, GCP, or Azure), object storage (e.g., S3, GCS). Familiarity with big data frameworks (Spark, Hadoop) and distributed computing. Solid understanding of data modeling, partitioning, and performance optimization. Comfort with CI/CD workflows, version control (Git), Docker, and Kubernetes. Nice to Have: Experience with real-time data pipelines using Kafka, Flink, or Spark Streaming. Exposure to ML data lifecycle: labeling, augmentation, training, evaluation. Knowledge of data governance, security, PII compliance, and auditability. Prior leadership experience or ownership of major data initiatives.

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Key Responsibilities: Manage end-to-end delivery of technical projects, with a focus on AWS cloud-based environments. Collaborate with cross-functional teams including engineering, DevOps, QA, and product teams. Own client communication, project updates, and stakeholder alignmentespecially with US-based customers. Implement and champion Agile practices—sprint planning, stand-ups, reviews, retrospectives. Identify and mitigate project risks proactively while ensuring timely delivery. Track and report progress, budgets, and timelines using tools like JIRA, Trello, or MS Project. Ensure adherence to cloud architecture best practices, data security, and high performance. Lead continuous improvement initiatives through retrospectives and feedback loops. Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field. 6+ years of experience in technical project management, software engineering, or IT project delivery. Strong understanding of AWS services is mandatory , including EC2, S3, RDS, Lambda, CloudFormation, and CloudWatch. Solid grasp of SDLC, system architecture, APIs, databases, and cybersecurity principles. Familiarity with CI/CD pipelines, DevOps tools, and microservices architecture. Hands-on experience with Agile frameworks and project management platforms. Excellent communication and stakeholder management skills. Experience working with US clients is preferred. Preferred Skills: AWS Certifications (e.g., AWS Certified Solutions Architect or DevOps Engineer). PMP, CSM, or other recognized project management certifications. Budgeting and cost control experience. Knowledge of data privacy and compliance frameworks (e.g., SOC 2, ISO 27001, GDPR)