AI Architect

One of our prestigious client is looking for a hands-on AI Architect who bridges software development, data engineering, and advanced AI/ML. You will design and code AI solutions that leverage large language models, embeddings and retrieval‑augmented generation while ensuring robust data pipelines and integration with our software products. This role requires a strong foundation in software engineering and data engineering along with deep expertise in AI/ML.

Key Responsibilities:

1. Cross‑Disciplinary Solution Design

Engage with stakeholders to understand product requirements and architect end‑to‑end solutions incorporating data ingestion, model training, deployment and monitoring.

Design systems that combine traditional software components with AI/ML models and modern data engineering practices.

1. Hands‑On Development

Write and maintain production‑quality code across back‑end services, data pipelines and model inference layers, primarily using Python and at least one other language (e.g. .Net, Java).

Build RESTful or gRPC APIs to expose AI functionality and integrate with front‑end and other microservices.

1. Data Engineering & Pipeline Construction

Develop and manage data pipelines for ingestion, transformation and storage (using tools such as Apache Spark, Kafka, Airflow, SQL/NoSQL databases).

Work with vector databases and embedding stores for retrieval‑augmented systems.

Ensure data quality, schema management and robust ETL processes that support model training and inference.

1. Advanced AI/ML & LLM Expertise

Train, fine‑tune and deploy machine‑learning and deep‑learning models, including large language models (LLMs) and transformer architectures (e.g., GPT, Llama, Mistral).

Implement embedding techniques (e.g., sentence and document embeddings), retrieval‑augmented generation (RAG) pipelines and hybrid search (vector + keyword).

Build customised pipelines using frameworks like LangChain, LLamaIndex or similar to integrate models with diverse data sources.

1. System Optimisation & Scalability

Optimise model performance (latency, throughput and accuracy) for production workloads, using techniques such as model quantisation, batching and caching.

Scale services using containerisation (Docker) and orchestration (Kubernetes) on cloud platforms (AWS, Azure, GCP).

1. Collaboration & Mentorship

Collaborate with software engineers, data scientists and DevOps teams to deliver cohesive solutions.

Mentor junior engineers and share best practices in software development, data engineering and AI/ML.

1. Monitoring & Maintenance

Implement observability for data pipelines and AI services (logging, metrics, model drift detection).

Continuously monitor and iterate models and pipelines based on feedback and new data.

Required Skills & Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence or related field.
• Experience: Minimum 6–8 years in software development and data engineering, with at least 3 years of hands‑on AI/ML experience.
• Proficiency in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit‑learn) and LLM libraries (e.g., Hugging Face Transformers, LangChain).
• Hands‑on experience building and deploying large language models, embeddings, RAG pipelines and vector‑database‑powered applications.
• Strong knowledge of data engineering tools (Spark, Kafka, Airflow), relational and NoSQL databases, and proficiency in SQL.
• Experience designing and operating microservices, APIs and containerised applications on cloud platforms.
• Solid understanding of MLOps best practices (CI/CD for ML, model versioning, automated testing, monitoring).
• Strong problem‑solving and communication skills; ability to translate business requirements into technical designs.

Preferred Qualifications:

• Experience with GPU orchestration (NVIDIA Triton, KServe) for model serving.
• Knowledge of privacy‑preserving techniques and responsible AI practices.
• Exposure to reinforcement learning or recommendation systems.

What We Offer:

The chance to work on cutting‑edge AI projects integrating software, data and advanced models.

A collaborative environment where hands‑on coding and architectural leadership go hand in hand.

Support for ongoing learning and professional development in AI/ML and data engineering.

Health Insurance

Gadget Allowance