AI/ML Engineer

Job Title: AI/ML Engineer

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About The Role

AI/ML Engineer

Generative AI

Large Language Models (LLMs)

Retrieval-Augmented Generation (RAG)

Key Responsibilities

• Design, develop, and implement Generative AI solutions leveraging LLMs, RAG, LangChain, and related frameworks.
• Build, fine-tune, and optimize models using TensorFlow, PyTorch, and Keras for various business use cases.
• Develop and maintain end-to-end AI/ML pipelines including data ingestion, preprocessing, model training, deployment, and monitoring.
• Implement Prompt Engineering techniques to improve LLM-based solutions.
• Work with Vector Databases for retrieval and contextual memory integration.
• Collaborate with cross-functional teams to integrate AI models into production environments via APIs (FastAPI/Python).
• Leverage cloud platforms (AWS, GCP, Azure) for scalable AI solution deployment and MLOps implementation.
• Utilize big data technologies such as Hadoop, Spark, or Apache Flink to build efficient data pipelines.
• Apply Deep Learning and Machine Learning techniques to solve complex business challenges.
• Contribute to MLOps practices including containerization (Docker/Kubernetes) and continuous model lifecycle management.
• Participate in model evaluation, optimization, and performance tuning.
  

Mandatory Skills

• Generative AI (Gen AI), LLMs, RAG, LangChain, Llama
• AI/ML, Deep Learning, Python
• TensorFlow, PyTorch, Pandas
• Prompt Engineering, Vector Databases
• MLOps
  

Preferred Skills

• Cloud Platforms: AWS, GCP, or Azure
• GPT-4 or similar LLMs
• SQL, FastAPI/API Development
• Docker, Kubernetes
• Hadoop, Spark, or Apache Flink (Data Pipeline)
• Banking or Financial Domain Exposure
  

Educational Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  

Required Expertise

• Strong proficiency in Python (and optionally R).
• Hands-on experience with AI/ML frameworks: TensorFlow, PyTorch, Keras.
• Solid understanding of Machine Learning algorithms and Deep Learning architectures.
• Experience in data analysis, data visualization, and statistical modeling.
• Familiarity with applied statistics, algorithm development, and model evaluation techniques.
• Awareness of containerization and deployment automation using Docker/Kubernetes.
  

Soft Skills

• Strong analytical and problem-solving skills with the ability to think critically and creatively.
• Excellent verbal and written communication skills with the ability to explain complex technical concepts clearly.
• Collaborative mindset with the ability to work effectively in cross-functional teams.