7 - 12 years

4 - 8 Lacs

Posted:10 hours ago| Platform: Foundit logo

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Work Mode

On-site

Job Type

Full Time

Job Description

We are looking for experienced and visionary engineers who are passionate about building AI-powered applications that deliver real-world business value at scale. As a Staff AI Engineer in our AI Acceleration team, you will lead the design and deployment of robust, scalable AI systems. This role requires a deep understanding of software engineering, strong architectural thinking, and hands-on experience integrating modern AI techniques into production-grade solutions.

Responsibilities:

  • Design, build, and maintain AI-powered applications and services using best practices in software engineering such as CI/CD, testing, observability, and cloud-native architecture
  • Architect and implement reusable and scalable AI platforms such as conversational bots, AI-powered search engines, GenBI tools, and systems for unstructured data processing
  • Make key architectural decisions and define standards for responsible AI, system explainability, and end-to-end lifecycle management
  • Lead embedded initiatives with business stakeholders to quickly prototype and deploy AI solutions addressing high-priority problems
  • Evaluate, select, and integrate modern AI frameworks, tools, and APIs including LLMs, vector databases, RAG, embeddings, and multi-agent systems
  • Champion automation across AI workflows, including data ingestion, preprocessing, model integration, evaluation, and deployment
  • Define success criteria, operational metrics, and standard operating procedures for AI systems in production
  • Collaborate closely with engineers, data scientists, product managers, and business partners to ensure the reliability and value of deployed AI solutions
  • Stay informed on emerging AI technologies and best practices, with a focus on practical implementation and enterprise scalability
  • Mentor junior engineers and data scientists, providing technical guidance and leadership across projects
  • Contribute to internal libraries, documentation, and tooling to elevate engineering practices across the organization

Required Qualifications:

  • 7+ years of professional software engineering experience with a proven track record of independently delivering complex systems
  • Strong programming proficiency in Python (preferred), Java, or similar languages, and experience building microservices, APIs, and scalable backend systems
  • Solid understanding of cloud infrastructure (AWS, Azure, or GCP), modern DevOps practices, and software architecture
  • Experience integrating and deploying machine learning models in production, including LLMs, RAG, embeddings, and agentic systems
  • Deep familiarity with LLMs, vector-based search, and unstructured data processing
  • Ability to ship features quickly without compromising code quality or operational stability
  • Strong problem-solving skills, leadership capabilities, and experience navigating ambiguity
  • Proven track record of technical mentorship and influencing cross-functional teams
  • Excellent communication skills with the ability to bridge technical and business perspectives

Preferred Qualifications:

  • Experience with AI/ML tools such as LangChain, Haystack, Hugging Face, or Weaviate
  • Hands-on knowledge of GenAI and agentic orchestration frameworks like LlamaIndex, Crew AI, or AutoGen
  • Experience building reusable ML components or contributing to internal ML platforms
  • Background working with embedded or forward-deployed teams in high-velocity environments
  • Proficiency in common ML and data science libraries such as scikit-learn, pandas, NumPy, PyTorch, or TensorFlow
  • Strong grasp of core ML concepts including supervised/unsupervised learning, statistical inference, and model evaluation
  • Experience working with unstructured data formats including documents, text, images, or conversations
  • Experience developing intelligent chatbots, search applications, or other generative AI interfaces
  • Familiarity with platform engineering or developing internal tools for engineering productivity

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