Senior AI Engineer
New
J
JobgetherEnterprise AI
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of hands-on AI/ML engineering experience
- Required Skills
- DockerPythonKubernetesPyTorchTensorflowscikit-learnLangChain
Requirements
- At least 5 years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Experience leading complex enterprise technical engagements with multiple stakeholders and cross-functional teams.
- Experience building and deploying LLM-powered applications, including RAG pipelines, agentic workflows, or fine-tuned models.
- Strong Python programming skills and practical experience with PyTorch, TensorFlow, and scikit-learn.
- Familiarity with LLM development frameworks such as LangChain or LlamaIndex, or equivalent technologies.
- Experience deploying AI services in enterprise or cloud environments, including AWS, Azure, GCP, or on-premises Kubernetes.
- Understanding of generative AI, prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Solid foundation in classical machine learning and selecting approaches based on business and technical requirements.
- Backend engineering experience, including REST APIs, Docker, Kubernetes, and CI/CD pipelines.
- Ability to coordinate multiple delivery streams, manage competing priorities, and guide teams through changing requirements.
- Experience communicating with stakeholders, presenting to executive audiences, and leading technical discussions.
- Advantageous experience includes Kaggle or competitive machine learning, enterprise AI platforms, AutoML, tabular foundation models, document AI, regulated industries, or customer-facing engineering, field engineering, or technical pre-sales.
Responsibilities
- Lead complex technical engagements with enterprise customers and manage delivery quality, stakeholder relationships, and project outcomes.
- Coordinate concurrent workstreams, resources, milestones, workplans, and cross-functional teams.
- Identify delivery risks and drive resolutions across engineering, product, and leadership teams.
- Build agentic AI systems and multi-agent frameworks for enterprise workflows.
- Design and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool integration.
- Implement AI guardrails, evaluation frameworks, and responsible AI controls.
- Own the AI development lifecycle from problem framing and data exploration through model development, API integration, and production deployment.
- Build scalable backend services, APIs, ML pipelines, and LLMOps infrastructure.
- Integrate AI solutions into cloud, on-premises, and hybrid environments.
- Lead architecture decisions, review engineering deliverables, mentor junior engineers, and lead customer demonstrations, workshops, and technical discussions.
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