Technical Architect (Python & AI)

1. Architect and design scalable, maintainable, and high-performance backend systems using Python.

2. Lead the development of clean, modular, and reusable code components and services across various domains.

3. Own the technical roadmap for Python-based services, including refactoring strategies, modernization efforts, and integration patterns.

4. Provide expert guidance on system design, code quality, performance tuning, and observability.

5. Collaborate with DevOps teams to build CI/CD pipelines, containerization strategies, and robust cloud-native deployment patterns.

6. Mentor and support software engineers by enforcing strong engineering principles, design best practices, and performance debugging techniques.

7. Evaluate and recommend new technologies or frameworks where appropriate, particularly in the areas of AI/ML and GenAI integration.

Preferred Qualifications:

Desirable (Good-to-Have) GenAI / AI/ML Skills

1. Exposure to Large Language Models (LLMs) and prompt engineering.

2. Basic familiarity with Retrieval-Augmented Generation (RAG) and vector databases (FAISS, Pinecone, Weaviate)

3. Understanding of model fine-tuning concepts (LoRA, QLoRA, PEFT)

4. Experience using or integrating LangChain, LlamaIndex, or Hugging Face Transformers

5. Familiarity with AWS AI/ML services like Bedrock and SageMaker

Technology Stack

Languages:

Web Frameworks:

Databases:

Cloud Platform:

DevOps & Deployment:

Observability:

GenAI Tools (Optional):

Preferred Profile

8+ years of hands-on software development experience

3+ years in a solution/technical architect or lead engineer role

Strong problem-solving skills and architectural thinking

Experience collaborating across teams and mentoring engineers

Passion for building clean, scalable systems and openness to learning emerging technologies like GenAI

Core Python & Architectural Skills

Strong hands-on experience in advanced Python programming (7+ years), including:

1. Language internals (e.g., decorators, metaclasses, descriptors)

2. Concurrency (asyncio, multiprocessing, threading)

3. Performance optimization and profiling (e.g., cProfile, py-spy)

4. Strong testing discipline (pytest, mocking, coverage analysis)

Proven track record in designing scalable, distributed systems:

1. Event-driven architectures, service-oriented and microservice-based systems.

2. Experience with REST/gRPC APIs, async queues, caching strategies, and database modeling.

Proficiency in building and deploying cloud-native applications:

1. Strong AWS exposure (EC2, Lambda, S3, IAM, etc.) Infrastructure-as-Code (Terraform/CDK) Private and Confidential

2. CI/CD pipelines, Docker, Kubernetes, GitOps

Deep understanding of software architecture patterns (e.g., hexagonal, layered, DDD)

Excellent debugging, tracing, and observability skills with tools like OpenTelemetry, Prometheus, Grafana