AIML Engineer

Job highlights

• Experience with Snowflake,PostgreSQL,Redshift,Databricks,or comparable data platforms.
• Experience delivering production-grade analytics,AI,and machine learning solutions in enterprise environments.
• Required Qualifications

Job Summary

Design, build, and deploy production-ready data analytics, machine learning, Generative AI, and agentic AI solutions that deliver measurable business outcomes.

Location: Gurugram, India

Experience: 4 to 10 years

Employment Type: Full Time

Work Model: Hybrid

Role Overview

We are looking for a hands-on AIML Engineer to develop scalable solutions across data analytics, machine learning, deep learning, Generative AI, agentic AI, and data engineering.

You will work across the complete AI lifecycle, from data preparation and model development to deployment, evaluation, monitoring, and continuous improvement. This role requires strong Python engineering skills and demonstrated experience delivering production-grade AI solutions.

Key Responsibilities

• Translate business challenges into practical analytics, AI, and machine learning use cases with measurable success criteria.
• Analyse structured and unstructured data to identify patterns, generate insights, engineer features, and support model development and business decisions.
• Design, build, and deploy machine learning and deep learning models using Python and modern AI frameworks.
• Develop Generative AI applications using OpenAI, Azure OpenAI, Gemini, Claude, or equivalent enterprise LLM platforms.
• Build RAG pipelines, semantic-search solutions, embedding workflows, and vector-based retrieval systems.
• Design tool-enabled agentic and multi-agent workflows using LangChain, LangGraph, LlamaIndex, CrewAI, or similar frameworks.
• Develop scalable data pipelines using SQL, Spark, PySpark, Kafka, Airflow, and modern data platforms.
• Establish evaluation frameworks covering model accuracy, relevance, safety, reliability, latency, and cost.
• Deploy and manage AI solutions using cloud platforms, Docker, CI/CD, and MLOps or LLMOps practices.
• Monitor model performance, data drift, failures, security risks, and production quality.
• Apply responsible AI practices covering privacy, explainability, governance, human oversight, and secure AI development.
• Collaborate with product, data, engineering, security, and business teams throughout the solution lifecycle.

Required Qualifications

• 4 to 10 years of experience in artificial intelligence, machine learning, data analytics, data science, or data engineering.
• Strong hands-on experience developing production-ready AI solutions using Python.
• Strong knowledge of exploratory data analysis, statistical modelling, feature engineering, machine learning, deep learning, and model evaluation.
• Experience with Scikit-learn, TensorFlow, PyTorch, XGBoost, LSTM, or comparable frameworks.
• Hands-on experience with LLMs, prompt engineering, RAG, embeddings, semantic search, and vector databases.
• Experience with agentic AI frameworks and tool-enabled or multi-agent workflows.
• Proficiency in SQL and experience building scalable data-processing pipelines.
• Experience with AWS, Microsoft Azure, or Google Cloud.
• Knowledge of Docker, MLflow, SageMaker, CI/CD pipelines, or comparable MLOps technologies.
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Experience delivering production-grade analytics, AI, and machine learning solutions in enterprise environments.
• Experience with NLP, document intelligence, OCR, graph analytics, computer vision, or time-series modelling.
• Familiarity with model fine-tuning, AI observability, guardrails, evaluation, and cost-performance optimisation.
• Experience with Snowflake, PostgreSQL, Redshift, Databricks, or comparable data platforms.
• Experience in financial services, banking, insurance, healthcare, or another regulated industry.
• Demonstrated success delivering measurable business outcomes through data analytics, AI, and machine learning.

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