AI / ML Engineer

AI / ML Engineer  – 3 to 5 yrs

6 to 8 yrs

NP : Immediate to 1 week (official) |  Location :  Gurgaon

Job Description – AI / ML Engineer

We are seeking a highly skilled  AI / ML Engineer  with strong expertise in  Python programming, API development, and real-time deployment of ML models . The ideal candidate should have experience in designing, building, and optimizing machine learning pipelines and integrating models into production environments. Key Responsibilities

Design, develop, and optimize  machine learning models  using Python and popular ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).

Implement  end-to-end ML pipelines  including data pre-processing, feature engineering, training, evaluation, and deployment.

Build and manage  RESTful APIs / gRPC services  to expose ML models for real-time inference.

Deploy and scale models in  production environments  (Docker, Kubernetes, cloud platforms such as AWS, GCP, or Azure).

Ensure  high availability, low-latency, and fault-tolerant  real-time ML systems.

Collaborate with data engineers and software teams to integrate ML solutions into existing applications.

Conduct  performance monitoring, optimization, and retraining  of models as needed.

Apply  MLOps best practices  for CI / CD pipelines, model versioning, and automated deployment workflows.

Write clean, efficient, and  production-grade Python code  following software engineering best practices.

Required Skills & Experience

3-8 years  of hands-on experience in  Python programming  (advanced knowledge of data structures, OOP, multiprocessing, async programming).

Strong expertise in  machine learning algorithms , model training, and evaluation techniques.

Experience with  API development  (FastAPI, Flask, Django, or similar).

Proven experience in  real-time model deployment  and serving (TensorFlow Serving, TorchServe, MLflow, or custom solutions).

Solid understanding of  cloud-native deployments  (AWS Sagemaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).

Knowledge of  streaming data frameworks  (Kafka, Spark Streaming, Flink) is a plus.

Familiarity with  CI / CD pipelines  for ML (GitHub Actions, Jenkins, or similar).

Strong grasp of  data engineering concepts  (data ingestion, transformation, and storage).

Experience in  monitoring & logging  (Prometheus, Grafana, ELK, or equivalent).

Nice to Have

Experience with  Generative AI (LLMs, diffusion models, transformers) .

Exposure to  GPU optimization and distributed training .

Familiarity with  feature stores  and advanced  MLOps frameworks  (Kubeflow, TFX, MLflow).

Note : AI / ML engineer

Python heavy

Live project hands on experience with deployment