Must Have Skills :

Core :

- 10 to 15 years of experience in software engineering .

- Strong programming skills in Python (mandatory).

- Experience in building production-grade ML systems (not just notebooks).

AI/ML & GenAI :

- Hands-on experience with :

- Data Analysis and curation

- Feature engineering

- Statistical model training, evaluation, and hyperparameter tuning

- LLMs / GenAI applications

- RAG pipeline design

- Prompt engineering & model tuning

- Experience with frameworks like Tensorflow, PyTorch, Sci-kit, LangChain LlamaIndex, or similar.

- Understanding of embeddings, vector search, and retrieval systems.

- Exposure to custom model fine-tuning (good to have, not mandatory).

MLOps & Deployment :

- Experience with :

- Model deployment (API-based or batch)

- CI/CD pipelines for ML

- Monitoring and logging

- Familiarity with tools like MLflow, Kubeflow, or similar (any one is fine).

Cloud & Scalability :

- Experience with at least one cloud: AWS / Azure / GCP.

- Understanding of scalable system design and APIs.

Data & Systems :

- Working knowledge of databases (SQL/NoSQL).

- Experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc.).

Good to Have (Optional) :

- Experience in AIOps or AI for observability/use-case automation.

- Background in data engineering or analytics pipelines.

- Exposure to Kubernetes/Docker.

- Experience in telecom or high-scale product environments.

Role Overview :

As a Principal AIML Architect, you will be at the forefront of designing and implementing cutting-edge AI and Machine Learning solutions.

Your day-to-day will involve collaborating closely with data scientists, engineers, and product managers to translate business requirements into scalable and robust AI architectures.

You will be responsible for guiding the technical direction of AI initiatives, ensuring alignment with business goals, and driving innovation in the application of AI technologies.

This role directly impacts our ability to deliver intelligent products and services, enhance customer experiences, and gain a competitive advantage through data-driven insights.

Key Responsibilities:

- Design and implement end-to-end AI/ML solutions, encompassing data ingestion, processing, model development, deployment, and monitoring, to address complex business challenges and drive measurable outcomes.

- Lead the development of data architectures and data modeling strategies that support AI/ML initiatives, ensuring data quality, accessibility, and security for effective model training and deployment.

- Provide technical leadership and mentorship to a team of data scientists and engineers, fostering a culture of innovation, collaboration, and continuous learning in the field of AI/ML.

- Evaluate and recommend appropriate AI/ML technologies, platforms, and tools, considering factors such as scalability, performance, cost-effectiveness, and integration with existing infrastructure, to optimize the AI development lifecycle.

- Collaborate with stakeholders across the organization, including product managers, business analysts, and IT teams, to define AI/ML use cases, gather requirements, and ensure alignment with business objectives.

- Develop and maintain comprehensive documentation of AI/ML architectures, design patterns, and best practices, enabling knowledge sharing and ensuring the long-term maintainability of AI solutions.

- Stay abreast of the latest advancements in AI/ML, including deep learning, LLMs, and cloud-based AI services, and evaluate their potential application to improve existing solutions or create new opportunities.

- Ensure compliance with relevant data privacy regulations and ethical guidelines in the design and implementation of AI/ML solutions, promoting responsible and trustworthy AI practices.

Required Skillset :

- Demonstrated ability to design and implement scalable and robust AI/ML solutions using Python and related libraries (e.g., TensorFlow, PyTorch, scikit-learn).

- Proven expertise in data modeling, data architecture, and data visualization techniques to effectively communicate insights and support data-driven decision-making.

- Deep understanding of machine learning algorithms, deep learning architectures, and LLMs, with the ability to select and apply appropriate techniques to solve specific business problems.

- Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services, including model deployment, monitoring, and scaling.

- Excellent communication, presentation, and interpersonal skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.

- Ability to lead and mentor a team of data scientists and engineers, fostering a collaborative and innovative environment.

- Possess a Bachelor's or Master's degree in Computer Science, Data Science, or a related field.

- Adept at working in a fast-paced, dynamic environment and adapting to changing priorities.