Full Stack Architect - Artificial Intelligence/Machine Learning (8-15 yrs)

Key Responsibilities :

Solution Design & Architecture :

- Architect scalable, secure, and cost-effective cloud-based and on-premise solutions to process IoT data efficiently.

- Design and integrate AI/ML models into enterprise systems, ensuring performance and scalability.

- Evaluate and select technologies, platforms, and frameworks to optimize IoT-based AI workflows.

AI/ML Development :

- Develop and deploy machine learning and deep learning models tailored to IoT data streams.

- Leverage advanced AI techniques, including transformers, autoencoders, and graph-based models, for predictive analytics and decision support.

- Fine-tune large language models (LLMs) for domain-specific tasks, integrating frameworks like Hugging Face and LangChain.

Data Engineering & Integration :

- Collaborate with data engineers to preprocess and structure IoT data pipelines for training and inference.

- Integrate diverse IoT data sources and implement efficient ETL pipelines for real-time and batch processing.

- Ensure seamless interaction between AI/ML systems, APIs, and enterprise platforms.

Performance & Scalability Optimization :

- Optimize IoT data processing and AI workflows for large-scale data handling using tools like Apache Spark and Hadoop.

- Implement MLOps practices, ensuring automated deployment pipelines and continuous model monitoring.

- Manage microservices and containerized deployments with Docker and Kubernetes.

Cross-functional Collaboration :

- Partner with teams to integrate AI/ML insights into IoT systems, enhancing traceability, maintenance, and operational efficiency.

- Work closely with business analysts, UX designers, and stakeholders to align technical solutions with business needs.

Innovation & Continuous Improvement :

- Stay updated with advancements in AI, IoT, and cloud technologies to drive innovation.

- Propose enhancements for deployed solutions based on performance metrics and feedback.

Technical Skills and Qualifications :

Education :

- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related fields.

Core Skills :

- Programming : Proficient in Python, R, or Java, with expertise in libraries like NumPy, Pandas, and TensorFlow.

- AI/ML Frameworks : Hands-on experience with TensorFlow, PyTorch, or JAX for developing advanced AI models.

- NLP & LLMs: Expertise in deploying and fine-tuning LLMs such as GPT-4, LLaMA, and similar tools.

- IoT Integration : Experience with IoT platforms and solutions for manufacturing analytics, predictive maintenance, and real-time data processing.

- Big Data Tools : Proficiency with Apache Spark, Kafka, and Hadoop for large-scale data processing.

- Cloud Platforms : Extensive experience with AWS, Azure, or GCP for AI/ML workload deployment.

- Database Expertise : Familiarity with SQL and NoSQL databases (e.g., MongoDB, Postgres).

Preferred Skills :

- Containerization : Proficiency in Docker and Kubernetes for scalable deployments.

- MLOps : Experience with MLflow, Kubeflow, and similar tools for model lifecycle management.

- Optimization Algorithms : Knowledge of simulation and optimization techniques for decision-making support.

- Security : Implementing robust security measures for IoT data and applications.

- Graph AI : Experience with graph embeddings and graph-based RAG for modeling complex relationships.

Soft Skills :

- Strong analytical and problem-solving abilities.

- Excellent communication skills for conveying technical concepts to non-technical stakeholders.

- Proven ability to work collaboratively across diverse teams.