Advanced Engineer: ML, LLM, NLP, RAG

About the Role

As an AI Technical Lead, you will own end-to-end delivery of production-grade AI systems, including LLM-powered applications, RAG pipelines, NLP solutions, and multimodal models involving audio and images. You will integrate these capabilities into engineering workflows to improve quality, speed, and traceability across the Car Audio development lifecycle.

This role is ideal for an engineer with strong implementation + SDLC exposure, who can guide architecture over time and lead a small team to deliver real, measurable outcomes.

What You Will Do

1.Lead AI-Enabled Tool Development for Car Audio SDLC (Systems / SW / Testing)

• Lead development of AI tools that improve productivity and quality across:

• Systems Engineering: requirements parsing, ambiguity detection, traceability automation, change impact analysis

• Software Engineering: code intelligence, defect/risk prediction, review assistants, documentation automation

• Testing & Validation: test case generation, log/trace analytics, failure clustering, intelligent triage, coverage optimization

• Drive tool adoption via measurable improvements (cycle time reduction, faster triage, improved quality metrics).

NLP, LLMs & RAG Applications

• Develop and fine-tune transformer-based models (BERT/GPT-class, etc.) for classification, similarity, and automation.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (FAISS, Pinecone, etc.).
• Implement prompt engineering, evaluation, and guardrails (privacy, safety, hallucination control).
• Build copilots/assistants for engineering teams (Systems/SW/Test) with strong reliability and auditability.

Deployment, MLOps & SDLC Integration

• Containerize AI services using Docker and support deployment on Kubernetes and/or cloud (AWS/Azure/GCP).
• Establish MLOps practices: experiment tracking, model registry/versioning, CI/CD for ML, monitoring, drift detection.
• Implement observability for AI tools (latency, failures, data drift, usage analytics); Prometheus/Grafana exposure is a plus.

Team Leadership

• Lead and mentor a team of ~5 engineers: planning, technical execution, code reviews, and delivery ownership.
• Coordinate with global stakeholders, manage priorities, and ensure predictable execution.
• Enforce engineering best practices (secure coding, testing, documentation, maintainability).

What You Need to Be Successful

• 8+ years of hands-on experience in AI/ML engineering with production deployments.

• Strong exposure to SDLC in large-scale product environments.

• Proven experience delivering AI-enabled tooling or platforms used by engineers.

• Bachelors/Master’s in CS/AI/Data Science or related field.

• Automotive/embedded exposure is a plus (Car Audio domain strongly preferred).

• Python (Advanced)

• PyTorch / TensorFlow

• NLP/LLMs: HuggingFace Transformers, orchestration frameworks, prompt engineering

• RAG + vector databases (FAISS/Pinecone/etc.)

• Multimodal ML:

• Audio processing libraries (e.g., librosa/torchaudio) and feature pipelines (e.g., mel-spectrograms)

• Image processing / vision models (OpenCV, CNN/ViT-based approaches)

• Multimodal embeddings and retrieval (audio/image/text)

• Docker, Git, Linux

• Cloud exposure (AWS/Azure/GCP), Kubernetes familiarity

• CI/CD fundamentals and monitoring/observability basics

Bonus Points if You Have

• Experience with audio ML in automotive (acoustic tuning/QoE, in-cabin signals, infotainment-related audio analytics)
• Multimodal LLM or VLM exposure (image+text understanding, document AI, multimodal RAG)
• Inference optimization (quantization, batching, GPU acceleration)
• MLOps tooling (MLflow or similar)
• Knowledge of ASPICE / ISO 26262 concepts, traceability, verification evidence automation