AI/ML Engineer

Uvation; Delhi; Remote; INR 12,00,000 - 18,00,000; Full time

Job summary

A leading tech company is seeking an AI/ML Engineer to develop and deploy machine learning models. The ideal candidate will have 3-5 years of experience, strong knowledge in Python and TensorFlow/PyTorch, and the ability to work with cross-functional teams on AI-driven solutions. This remote position offers opportunities for innovation in a dynamic environment.

Qualifications

• 3–5 years of hands-on experience in machine learning model development.
• Proven track record of solving real-world machine learning problems.
• Strong knowledge of Python and essential ML libraries.

Responsibilities

• Design, build, and deploy ML models for various applications.
• Clean, preprocess, and transform datasets for training.
• Monitor deployed models for performance in production.

Skills

Machine learning algorithms

Python

TensorFlow/PyTorch

Data preprocessing

Model evaluation

Tools

Docker

Flask/FastAPI

Kubernetes

MLflow

Airflow

Job description

Job Title: AI/ML Engineer
Department: IT Services
Reports To: IT Project Manager
Location: Delhi NCR (Remote)

Job 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.

The ideal candidate will have 3 to 5 years of experience in AI/ML model development, with a strong foundation in machine learning algorithms, data preprocessing, and deployment pipelines. Experience with Python, TensorFlow/PyTorch, and cloud-based ML services is essential.

Responsibilities:

1. Model Development and Optimization

• 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.

2. Data Preparation and Feature Engineering

• 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.

3. Model Deployment and MLOps

• 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.

4. AI Solutions and Use Case Implementation

• 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.

5. Research and Innovation

• 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.

6. Cross-functional Collaboration

• 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.

7. Documentation and Best Practices

• Maintain comprehensive documentation for models, experiments, and pipelines.
• Ensure reproducibility, scalability, and compliance with data governance policies.

Requirements:

Experience:

• 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.

Technical Skills:

Strong knowledge of:

• Python and ML libraries (scikit-learn, pandas, NumPy, TensorFlow/PyTorch)
• Model evaluation, hyperparameter tuning, and pipeline automation
• REST APIs for model serving and integration

Familiarity with:

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

Soft Skills:

• Strong analytical and problem-solving abilities.
• Excellent communication skills, both verbal and written.
• Ability to work independently and within cross-functional teams.
• Curiosity, adaptability, and willingness to learn continuously.