AI Ml Engineer

Photon Interactiveis currently expanding its engineering teams for large-scale digital transformation and enterprise product engagements. We are looking for passionate professionals who enjoy building scalable, high-performance, and user-centric applications in a fast-paced collaborative environment. While we work with large enterprises on digital products, their core business model revolves around providing services such as AI management, digital innovation, and, engineering excellence. At Photon, we are not just hiring AI Engineers but we are building teams that will drive the next wave of enterprise AI transformation across global Fortune 100/500 organizations.

Services Offered: Artificial Intelligence, Mobile application development, bespoke software development, and IT consulting.

Locations: Operating globally with major offices across India (Chennai, Bengaluru, Hyderabad, and Pune), the USA, Indonesia, France, Canada, the UK, Mexico, and Ireland.

Workmode: WFO all 5 days.

Job Location : Bangalore / Chennai / Hyderabad / Pune

Role Summary:

• Design, develop, and deploy AI/ML models using Python, TensorFlow, PyTorch, Keras, and Hugging Face frameworks.
• Build scalable solutions in NLP, Computer Vision, Predictive Analytics, and Generative AI applications.
• Perform data preprocessing, feature engineering, model training, evaluation, and optimization using Pandas, NumPy, and Spark.
• Deploy and monitor ML models using FastAPI, TensorFlow Serving, TorchServe, Triton, and cloud platforms like AWS/GCP/Azure.
• Collaborate with cross-functional teams and implement MLOps practices using CI/CD, MLflow, DVC, and Airflow pipelines.

Core Skills:

• Strong programming skills in Python
• Experience with ML frameworks: TensorFlow, PyTorch, Keras, JAX, Hugging Face

Expertise In:

• NLP, Computer Vision, Predictive Modeling, or Generative AI

Model Deployment:

• TensorFlow Serving, TorchServe, FastAPI, Triton

Data Processing:

• Pandas, NumPy, Spark

Fundamentals:

• Strong understanding of ML concepts, deep learning, and statistics

Cloud & MLOps:

• AWS / GCP / Azure
• CI/CD, MLflow, DVC, Airflow

Good to Have:

• Distributed training, vector databases (FAISS, Pinecone, Chroma), GPU acceleration
• Prompt engineering, RAG frameworks, multi-agent systems
• LLM fine-tuning (LoRA, QLoRA, PEFT)
• Real-time inference, model optimization, quantization
• Relevant certifications