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EXC Managed Services

Senior AI/ML Engineer - Unifyed

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Job Title: Senior AI/ML Engineer
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Experience Level: 6+ Years
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Employment Type: Full\-Time
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Location: Gurugram, Sector 33
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Shift Timings: 12:00 PM \- 9:00 PM IST
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About the Role:
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We are looking for a hands\-on Senior AI/ML Engineer who can own the full lifecycle of machine learning\nsolutions \u2013 from problem definition and data modelling to training, deployment, monitoring, and\ncontinuous improvement.\nYou should be comfortable working with messy real\-world data, designing robust data models &\nfeatures, building and training models, and shipping them to production with proper MLOps practices.\nYou must also be aware of the current AI/ML landscape (LLMs, embeddings, vector search, modern\ntooling) and know when to use what.
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Key Responsibilities:<\/b>
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End\-to\-End Solution Ownership <\/b><\/span>
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  • Work with product / domain stakeholders to understand business problems and define ML use\ncases
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  • Translate requirements into data & model design, success metrics, and clear technical plans
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  • Own the full pipeline: data ingestion → cleaning → feature engineering → model training →\nevaluation → deployment → monitoring<\/span>
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    Data Modelling & Feature<\/b> <\/span>
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    • Engineering\nDesign and maintain data models / schemas optimized for analytics and ML training (batch & real\ntime) <\/span>
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    • Perform exploratory data analysis (EDA) and feature engineering to improve signal quality and\nmodel performance <\/span>
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    • Work closely with data engineering to ensure reliable, well\-documented datasets<\/span><\/span>
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      Model Training & Evaluation<\/b> <\/span><\/span>
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      • Build, train, and tune models for tasks such as: prediction, classification, ranking, recommendations,\nanomaly detection, and NLP. <\/span><\/span>
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      • Use appropriate techniques (traditional ML, deep learning, embeddings, LLMs) based on the\nproblem <\/span><\/span>
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      • Define and track offline and online metrics; run A/B tests or controlled experiments where applicable<\/span><\/span><\/span>
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        MLOps & Productionization <\/b><\/span><\/span><\/span>
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        • Build reproducible training pipelines (e.g., using MLflow, Airflow, Kubeflow, or similar tools) <\/span><\/span><\/span>
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        • Package and deploy models as APIs / microservices or batch jobs, using containers and cloud\nservices <\/span><\/span><\/span>
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        • Implement monitoring, alerting, and logging for model performance, data drift, and system health <\/span><\/span><\/span>
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        • Manage model versions, rollouts, and rollback strategies<\/span><\/span><\/span><\/span>
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          AI/ML Architecture & Best Practices<\/b> <\/span><\/span><\/span><\/span>
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          • Evaluate and integrate modern AI tools: vector databases, embedding models, LLM APIs, RAG\narchitectures, etc.\nEnsure solutions follow security, privacy, and compliance best practices (e.g., PII handling, access\ncontrol) <\/span><\/span><\/span><\/span>
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          • Write clear documentation for data flows, models, and services <\/span><\/span><\/span><\/span>
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          • Mentor junior engineers/data scientists and contribute to engineering standards and guidelines<\/span><\/span><\/span><\/span><\/span>
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            Must\-Have Skills & Experience\nCore Technical Skills <\/b><\/span><\/span><\/span><\/span><\/span>
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            • (6+ Years)\nPython Programming: Strong expertise in ML libraries (pandas, numpy, scikit\-learn, PyTorch,\nTensorFlow) <\/span><\/span><\/span><\/span><\/span>
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            • SQL & Databases: Solid SQL skills and hands\-on experience with relational and NoSQL data stores <\/span><\/span><\/span><\/span><\/span>
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            • Production ML: Demonstrated experience shipping end\-to\-end ML projects to production (not just\nnotebooks / POCs) <\/span><\/span><\/span><\/span><\/span>
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            • ML Fundamentals: Deep understanding of supervised/unsupervised learning, evaluation metrics,\noverfitting, bias/variance, data leakage<\/span><\/span><\/span><\/span><\/span><\/span>
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              MLOps & DevOps<\/b> <\/span><\/span><\/span><\/span><\/span><\/span>
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              • Senior AI/ML Engineer\nExperiment tracking tools (MLflow, Weights & Biases) <\/span><\/span><\/span><\/span><\/span><\/span>
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              • Model versioning and packaging (Docker, virtualenv, Conda)\nCI/CD pipelines for ML services <\/span><\/span><\/span><\/span><\/span><\/span>
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              • Infrastructure as Code and containerization best practices<\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                Cloud & Architecture<\/b> <\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                • Proficiency with at least one major cloud platform:\nAWS: S3, EC2, SageMaker, Lambda, RDS, DynamoDB\nGCP: Cloud Storage, Compute Engine, Vertex AI, Firestore <\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                • Azure: Blob Storage, VMs, Azure ML, Cosmos DB\nAPI design (REST/GraphQL) and microservice architecture integration<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                • Understanding of scalability, latency, and cost optimization<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  Modern AI/ML Landscape Awareness<\/b> <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  Exposure to LLMs & embeddings (OpenAI, HuggingFace, Anthropic, etc.)\nFamiliarity with vector search & semantic search platforms (OpenSearch, Elasticsearch, Pinecone,\nWeaviate, pgvector) <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  Ability to make technical trade\-offs between classical ML vs deep learning vs LLM\-based approaches <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  Understanding of cost, latency, and accuracy considerations for each approach <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  Soft Skills\nProblem\-Solving<\/b><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  • Strong analytical thinking with ability to question requirements and propose\nbetter solutions <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  • Independence: Can drive projects from ideation through production deployment with minimal\nguidance <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  • Communication: Excellent at explaining technical trade\-offs and complex concepts to both technical\nand non\-technical stakeholders <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>
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                  • Collaboration: Works well with cross\-functional teams (product, data engineering, infrastructure,\nsecurity
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