Ford Motor Company संयुक्त राज्य अमेरिका की एक ऑटोमोबाइल निर्माता कंपनी है, जिसका इतिहास 1903 से जुड़ा है। वाहनों के विकास और उत्पादन के अलावा, कंपनी मोबिलिटी सेवाओं, डिजिटल अनुभवों और सॉफ़्टवेयर पर भी अपना ध्यान बढ़ा रही है। Ford के कार्यों में स्थिरता से जुड़े प्रयास और पर्यावरणीय लक्ष्य भी शामिल हैं, जो परिवहन, नवाचार, सामुदायिक प्रभाव और जिम्मेदार व्यावसायिक प्रक्रियाओं को जोड़ने वाले दृष्टिकोण को दर्शाते हैं।
AI/ML Architect at Ford Motor Company (Hybrid, India)
Build autonomous agents, multi-agent systems, and retrieval-augmented generation pipelines for enterprise use. Deploy production generative AI on Google Cloud using MLOps, containers, and vector databases.
विवरण
- Develop and deploy autonomous and semi-autonomous AI agents for enterprise processes.
- Design multi-agent architectures with orchestration, task breakdown, planning loops, and inter-agent communication.
- Connect agents to enterprise platforms, REST and GraphQL APIs, databases, and Google Cloud services.
- Build short-term, long-term, episodic, and semantic memory capabilities.
- Create, test, refine, and maintain prompt templates and reusable prompt libraries.
- Assess, choose, and fine-tune foundation models, including Gemini through Vertex AI Model Garden.
- Build and tune high-throughput, low-latency RAG pipelines, including document ingestion, chunking, and embedding generation.
- Integrate and operate scalable vector databases.
- Clean and prepare structured and unstructured data for use.
- Provision secure Google Cloud environments with Terraform infrastructure as code.
- Build and maintain CI/CD pipelines for automated tests, container builds, and deployment across environments.
- Package applications and agents with Docker, then deploy them on GKE or Cloud Run.
- Set up workflow orchestration with tools such as Vertex AI Pipelines or Cloud Composer/Airflow.
- Define metrics and testing pipelines to assess agent quality, latency, token use, and hallucination rates.
- Add content filtering, moderation, grounding checks, prompt-injection defenses, privacy controls, and human review gates.
- Set up production monitoring, logs, distributed tracing, and real-time alerts.
- Improve agent workflows, model settings, caching, and infrastructure for cost and performance.
- Manage Git branches, pull requests, and code reviews.
- Work with data scientists, software engineers, and business teams to deliver production-ready AI solutions.
- Document system architecture, agent flows, tools, prompts, containers, and operating procedures.
आवश्यकताएँ
- At least three years of professional experience in machine learning or AI engineering, with a solid MLOps foundation.
- One to two years of practical experience designing and deploying production LLM, generative AI, or agentic systems.
- Expert proficiency in Python.
- Experience with PyTorch, TensorFlow, or Scikit-learn.
- Strong theoretical and practical knowledge of deep learning, natural language processing, and Transformer architectures.
- Practical experience with LangChain, LangGraph, LlamaIndex, CrewAI, or Vertex AI Agent Builder.
- Demonstrated experience with Google Cloud Platform, Docker, Kubernetes/GKE, Cloud Run, and Cloud Functions.
- Demonstrated experience with vector indexing, semantic search, Vertex AI Vector Search, AlloyDB, or comparable tools.
- Knowledge of SQL and NoSQL databases, REST APIs, and GraphQL APIs.
- Experience with Terraform, Git, and CI/CD tools such as Cloud Build, GitLab CI, or Jenkins.
- Experience with Prometheus, Cloud Logging, or LangSmith, and with evaluating LLM output quality and safety.
- Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification is highly desirable.
- Bachelor’s degree or equivalent.