Lead AI / ML Software Engineer

The Company

Cableteque comprises an international team with extraordinary wire interconnects, CAD deployments, and AI / ML expertise. Our team is located worldwide, including the US, Europe, and Asia. This enables us to assemble diverse perspectives, comprehensive knowledge, and specialized expertise to deliver the best software products and services to our customers.

Cableteque specializes in offering Predictive Interconnect Analytics (PIA) as a SAAS solution for the electronics OEM industry to address challenges in interconnect design. PIA provides comprehensive design optimization, CAD validation, subject-matter expertise, and design enhancements for complex interconnect systems, ultimately improving effectiveness and predictability. By partnering with industry key players, Cableteque helps OEMs focus on the interconnect's purpose while reducing the risk of costly mistakes and delays in the product design cycle.

Role Overview

We are seeking a Lead AI / ML Software Engineer to join our globally distributed team. In this role, you will lead the design and implementation of machine learning pipelines and AI-driven solutions that power next-generation interconnect design to manufacturing tools. You will work across both back-end and front-end systems to deliver robust, scalable, and intelligent software solutions.

Responsibilities

Design, develop, and deploy ML models, data pipelines, and intelligent analytics for the PIA platform.

Architect end‑to‑end ML workflows : data ingestion, training, evaluation, deployment, monitoring.

Partner with CAD, data, and product teams to integrate ML-driven decision systems into engineering workflows.

Build and maintain backend services in Python (FastAPI) and Java (Spring Boot).

Contribute to React front-end components for visualization and interactive ML insights.

Apply MLOps best practices (experiment tracking, CI / CD for models, automated retraining) using tools such as MLflow / Kubeflow / Airflow / SageMaker.

Ensure scalability, reliability, and security across cloud environments (AWS / GCP / Azure).

Participate in code reviews, system design, and cross‑functional technical planning.

Qualifications

Assessment : Candidates will complete a pre‑interview technical assignment.

Overall software engineering : 5–10 years professional experience (min 5 years).

Applied AI / ML development : 4–7 years (min 4 years) delivering models to production.

Applied Transformers and embedding-based models : 2+ years, including RAG systems and vector databases (e.g., Milvus, Pinecone).

Deep learning : 2+ years with PyTorch , TensorFlow , or Keras (production deployments).

MCP : 1+ years.

OCR : 1+ years.

Python : 5–10 years (min 5 years) with Python 3.x for data / ML services.

MLOps & pipelines : 2+ years implementing training / inference pipelines, versioning, and monitoring using MLflow , Kubeflow , Airflow , or SageMaker .

Cloud : Commercial experience on at least one major cloud ( AWS 3+ years or Azure / GCP 2+ years).

Data tooling : Proficiency with NumPy , Pandas , scikit-learn ; SQL proficiency.

Communication : Professional English proficiency for distributed teamwork.

Education & Certifications

Required : Bachelor’s degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or related field.

nice‑to‑have : Degrees from top Indian institutes (e.g., IIT / IIIT / VIT) or equivalent.

Certifications (nice‑to‑have) : AWS Certified ML – Specialty, Google Professional ML Engineer, or equivalent.

The candidates might be required to complete an assignment as a prerequisite for the interview.

Good luck