Description :

We are seeking a highly skilled and innovation-driven AI/ML Engineer to design, build, and deploy advanced machine learning and Generative AI solutions.

The ideal candidate will have a deep understanding of machine learning principles, hands-on expertise with modern AI/ML technologies, and proven experience delivering production-grade models and scalable data pipelines.

This role requires a strong engineering mindset, the ability to work on end-to-end ML lifecycles, and the capability to collaborate with cross-functional product, engineering, and data teams to drive measurable business impact.

Key Responsibilities :

Machine Learning & Deep Learning Development :

- Design, develop, and optimize ML models using supervised and unsupervised techniques including regression, classification, clustering, neural networks, and time-series models.

- Build and train deep learning architectures using TensorFlow, PyTorch, and Keras.

- Implement feature engineering, model validation, hyperparameter tuning, and model interpretability methods.

Generative AI & LLM Engineering :

- Develop solutions using cutting-edge Generative AI and LLM technologies such as GPT, Llama, Claude, or similar frameworks.

- Build prompt engineering pipelines for text generation, summarization, classification, embeddings, and conversational AI.

- Fine-tune or adapt foundation models using techniques such as transfer learning, LoRA, RAG, quantization, and prompt-based learning.

Data Engineering & Pipeline Automation :

- Build scalable data preprocessing and transformation pipelines using Python, PySpark, Databricks, or similar platforms.

- Work with structured, semi-structured, and unstructured data including text, audio, and image datasets.

- Implement CI/CD pipelines for ML using tools such as MLflow, Kubeflow, or Azure/ AWS/ GCP ML services.

Prototyping & Experimentation :

- Lead rapid prototyping and experimentation to evaluate new ideas, models, algorithms, and architectures.

- Perform A/B testing, model benchmarking, and metric-driven performance enhancements.

- Translate research concepts into functional engineering implementations.

Model Deployment & Productionization :

- Deploy ML models and AI systems into production environments using APIs, microservices, or cloud platforms.

- Implement monitoring, retraining pipelines, and model performance dashboards.

- Ensure scalability, low latency, and reliability of ML-driven services.

Collaboration & Documentation :

- Work closely with data scientists, backend engineers, cloud teams, and product owners to deliver end-to-end solutions.

- Create technical documents, architecture diagrams, and best-practice guidelines for ML systems.

- Adhere to secure coding, compliance, and governance standards for AI systems.

Mandatory Skills :

- 5- 8 years of total experience, including 2- 5 years in software engineering and a minimum of 2 years in AI/ML project delivery.

- Strong understanding of ML fundamentals : supervised and unsupervised learning, regression techniques, classification models, clustering, and neural networks.

- Hands-on experience with Python, scikit-learn, TensorFlow, PyTorch, NumPy, Pandas, and ML experimentation libraries.

- Exposure to Databricks, PySpark, MLflow, or equivalent platforms.

- Experience working with Generative AI, LLMs, embeddings, RAG pipelines, vector databases, and prompt engineering.

- Proven experience in rapid prototyping, iterative model development, and POC-to-production transitions