AI/ML Engineer - Data Modeling

Role : ML/AI Engineer

We are seeking a highly skilled Machine Learning / AI Engineer with strong technical expertise in building, deploying, and scaling ML and deep learning solutions. The ideal candidate should have 4+ years of hands-on experience working with advanced AI models, modern ML frameworks, and cloud-native environments. This role requires strong problem-solving skills, end-to-end model lifecycle ownership, and the ability to translate business requirements into cutting-edge AI-driven solutions.

Key Responsibilities :

Model Development & Training :

- Build and fine-tune neural network architectures (CNNs, RNNs, LSTMs, Transformers, GANs) based on use case requirements.

- Implement feature engineering, data preprocessing pipelines, and advanced techniques such as embeddings, transfer learning, and dimensionality reduction.

Model Deployment & MLOps :

- Deploy ML models at scale using Docker, Kubernetes, MLflow, Kubeflow, or SageMaker.

- Implement CI/CD pipelines for ML including automated testing, monitoring, and retraining strategies.

- Optimize models for latency, throughput, and cost efficiency in production-grade environments.

Data Engineering & Management :

- Build robust ETL/ELT pipelines to handle large-scale structured and unstructured datasets.

- Work with SQL/NoSQL databases, data warehouses (Snowflake, BigQuery, Redshift), and data lakes.

- Leverage cloud-native data services (AWS S3/Glue, Azure Data Lake, GCP BigQuery).

Generative AI & NLP (Preferred) :

- Experience with LLMs, RAG pipelines, prompt engineering, fine-tuning transformers (BERT, GPT, LLaMA, T5, etc.).

- Implement text embeddings, semantic search, and vector databases (Pinecone, Weaviate, FAISS, Milvus).

Performance Optimization & Research :

- Conduct hyperparameter tuning and leverage distributed training frameworks (Horovod, DeepSpeed, Ray).

- Explore and integrate emerging ML/AI techniques to improve accuracy, interpretability, and scalability.

Collaboration & Documentation :

- Partner with cross-functional teams (data engineers, product managers, business analysts) to design AI-driven solutions.

- Document architecture, data pipelines, experiments, and model performance for knowledge sharing and compliance.

Required Skills & Experience :

- 4+ years of hands-on experience in ML/AI model development and deployment.

scikit-learn, Keras, Hugging Face Transformers.

- Proficiency in data manipulation tools (Pandas, NumPy, Spark, Dask).

- Hands-on expertise in cloud platforms (AWS, Azure, GCP) and their AI/ML offerings.

- Experience with MLOps tools : MLflow, Kubeflow, Airflow, or Vertex AI.

- Knowledge of DevOps practices : Docker, Kubernetes, Git, CI/CD pipelines.

- Strong understanding of statistics, probability, linear algebra, and optimization methods.

- Familiarity with security, data governance, and compliance in AI/ML systems.

Preferred Qualifications :

- Experience with Generative AI, LLMs, and advanced NLP techniques.

- Knowledge of computer vision frameworks (OpenCV, Detectron2, YOLO, Hugging Face Diffusion models).

- Exposure to reinforcement learning and time-series forecasting models.

- Research background with publications/patents in ML/AI.

- Contribution to open-source ML projects or Kaggle competitions.