MODULE LEAD - Python-Langchain

AI Developer Workflow Automation

We are seeking an AI Developer to design, build, and deploy intelligent automation solutions that enhance software and embedded development workflows. This role combines expertise in Large and Small Language Models with developer tooling, test automation, and embedded systems. You will work at the intersection of AI/ML, software engineering, and embedded firmware to deliver scalable, high-impact productivity solutions.

Workflow Automation

Design and implement AI-driven pipelines for automated code generation from technical specifications, requirements, and documentation.

Develop intelligent systems for processing, analyzing, and synthesizing technical documents.

Create solutions to automate repetitive software development and testing tasks.

Integrate LLM/SLM-powered tools into IDEs and developer workflows to boost engineering productivity.

Implement Retrieval-Augmented Generation (RAG) systems for efficient knowledge management across codebases and documentation.

Small Language Model Development for Embedded Test Automation

Develop, fine-tune, and deploy compact Small Language Models (1B?7B parameters) to automate the generation, execution, and validation of embedded test cases.

Fine-tune SLMs (e.g., Llama 3.x, Gemma, Microsoft Phi series) using techniques such as LoRA and QLoRA on embedded-specific datasets including:

o C header files

o Firmware requirements

o Historical test cases and logs

Build RAG-based test generation pipelines that convert embedded requirements into:

o Executable C test cases

o Python-based test frameworks (e.g., PyTest, Cantata)

o Simulation and validation scripts

Adapt model outputs to project-specific APIs, data structures, and coding standards to minimize hallucinations and ensure syntactic correctness.

Generate C-code skeletons, stubs, and protocol-specific test logic (CAN, SPI, UART, I2C, etc.).

Integration, Deployment & Optimization

Integrate AI-based test generation tools into CI/CD pipelines to automatically create tests for new features, edge cases, and regressions.

Optimize models for low-latency inference and efficient resource usage, enabling local or edge deployment for secure environments.

Prototype and evaluate new AI-driven approaches to improve embedded and software test coverage.

Required Qualifications

Technical Skills

Strong proficiency in Python and experience with deep learning frameworks such as PyTorch and Hugging Face .

Hands-on experience with LLMs/SLMs , RAG systems, and AI orchestration frameworks (LangChain, Haystack, or similar).

Solid understanding of fine-tuning techniques (LoRA, QLoRA) and model optimization.

Experience with REST APIs, automation frameworks, and developer tooling.

Strong command of Git and collaborative development workflows.

Embedded & Systems Knowledge

Good understanding of embedded systems development , including:

o C/C++ programming

o Microcontrollers and firmware architecture

Familiarity with embedded testing methodologies and tools is a strong plus.

Experience developing IDE extensions or developer productivity tools (VS Code, IntelliJ, etc.).

Knowledge of CI/CD pipelines and automated testing frameworks.

Experience with vector databases and semantic search.

Exposure to MLOps practices, model deployment, and inference optimization.

Background in developer tooling, workflow automation, or embedded test engineering.

Education

Bachelor?s or Master?s degree in Computer Science, Computer Engineering, Electronics, AI/ML, or a related field .

Relevant academic or practical experience in machine learning, NLP, software automation, or embedded systems.