Posted today · be early

AI/ML Engineer

uvation

IndiaremotePosted 1 day ago

Skill Required

AI-ML-EngineerMachine-Learning-EngineerData-ScienceMLOps-EngineerAI-Solutions-EngineerAI-ML-Software-EngineerAI-ML-Engineer-JobsAI-ML-Engineering-JobsAI-ML-Services-EngineerAI-ML-Cloud-EngineerMachine Learning EngineerAI EngineerAIMachine Learningsoftware engineeringComputer VisionDeep LearningGenerative AIScikit-learnCD pipelinesLangChainWeb AccessibilityKubernetesEngineeringTensorFlowanalyticalautomationdevelopingData StructuresdesigninganalyticsMLflowPyTorchFastAPIAirflowwrittenPythonContract

Key highlights

3–5 years of required AI/ML experience.
Proficiency in Python and major ML libraries (scikit‑learn, pandas, NumPy, TensorFlow/PyTorch).
Experience with MLOps tools such as Docker, Kubernetes, MLflow, and Airflow.
Ability to design, develop, and deploy models for classification, regression, NLP, computer vision, and time‑series forecasting.
Strong analytical, problem‑solving, and communication skills.
Collaboration with cross‑functional teams including software developers, DevOps, and data analysts.

Role overview

The AI/ML Engineer plays a critical role in designing, developing, and deploying machine learning models and AI-driven solutions to support strategic business initiatives. The role involves collaborating with cross-functional teams, including software engineering, data analytics, product development, and business stakeholders, to drive intelligent automation, data-driven decision-making, and advanced analytics capabilities.

Responsibilities

Design, build, and deploy ML models for classification, regression, NLP, computer vision, or time-series forecasting.
Select appropriate algorithms and techniques based on business needs and data characteristics.
Continuously monitor and improve model performance using metrics and feedback loops.
Clean, preprocess, and transform structured and unstructured datasets for training and inference.
Engineer and select relevant features to improve model accuracy and generalizability.
Collaborate with data engineers to ensure data quality and accessibility.
Package and deploy models using tools like Docker, Flask/FastAPI, and Kubernetes.
Implement CI/CD pipelines for ML using platforms like MLflow, Airflow, or Kubeflow.
Monitor deployed models for drift, latency, and performance in production environments.
Work with business stakeholders to translate real-world problems into AI/ML use cases.
Prototype and test AI-driven solutions (e.g., recommendation engines, chatbots, fraud detection).
Contribute to proof-of-concept projects and assist in scaling successful models to production.
Stay updated with the latest research, frameworks, and tools in machine learning and AI.
Experiment with cutting-edge models (e.g., LLMs, transformers, generative AI) and assess their viability.
Promote innovation by recommending and implementing modern AI strategies.
Collaborate with software developers, DevOps, data analysts, and domain experts for end-to-end solution delivery.
Translate technical insights into business value through clear documentation and presentations.
Maintain comprehensive documentation for models, experiments, and pipelines.
Ensure reproducibility, scalability, and compliance with data governance policies.

Requirements

3–5 years of hands‑on experience in machine learning model development and deployment.
Proven track record of solving real‑world problems using supervised, unsupervised, or deep learning methods.
Strong knowledge of Python and ML libraries (scikit‑learn, pandas, NumPy, TensorFlow/PyTorch).
Strong knowledge of model evaluation, hyperparameter tuning, and pipeline automation.
Strong knowledge of REST APIs for model serving and integration.
Strong analytical and problem‑solving abilities.
Excellent verbal and written communication skills.
Ability to work independently and within cross‑functional teams.
Curiosity, adaptability, and willingness to learn continuously.

Nice to have

Familiarity with MLOps tools (MLflow, Airflow, DVC, Docker, Kubernetes).
Familiarity with cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform).
Familiarity with NLP or computer vision frameworks (e.g., Hugging Face, OpenCV).