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You will own the inference and orchestration layer that powers AI interactions, ensuring high reliability and low latency. Your role involves building production systems, designing inference pipelines, and managing monitoring and incident response.
You will build end-to-end product features and design agent workflows that handle planning, tool use, and failure recovery. You will collaborate with ML and backend teams to integrate LLMs and memory into reliable, production-grade systems.
You will research and define end-to-end AI system requirements while balancing model capabilities, latency, and user impact. You will also collaborate closely with engineering teams to design, evaluate, and iterate on AI-powered features to ensure reliability and user trust.
You will build and evolve the agent harness by implementing hooks, permission gates, and context compaction to ensure reliable task completion. Additionally, you will architect planner/executor/evaluator pipelines and manage agent memory to support long-horizon, multi-session execution.
![Smart Working Solutions is hiring for remote Senior Full Stack / Applied AI Engineer (Remote, Full-Time), India [AS216]](/assets/08150176-369d-44db-a4a7-778571334971.png){kind=small}
You will design and build end-to-end features, translating ambiguous stakeholder requirements into scalable, production-ready solutions. Additionally, you will improve existing Python-based applications and integrate AI-driven services to enhance product functionality.
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Design and implement AI-powered features such as automation and copilots by integrating LLMs and machine learning models into existing services. Collaborate with product teams to translate business requirements into scalable, reliable, and cost-efficient AI-driven solutions.
The Senior Systems Solutions Architect designs, implements, and optimizes enterprise-grade hybrid cloud, virtualization, and AI-driven infrastructure solutions. This role also provides ongoing technical support, client consulting, and strategic guidance to modernize IT environments.
Design and deploy end-to-end AI-powered automation workflows for insurance and real estate operations. Integrate various CRM, policy management, and document processing systems using APIs and AI tools to replace manual processes.
You will own the product roadmap, platform strategy, and execution for AI-powered ordering capabilities while managing a small team of product managers. You are expected to lead discovery, prototype solutions, and design automated workflows to maximize product velocity and team output.
The Senior AI Engineer will design, build, and deliver AI-powered features by integrating large language models into production systems. They will collaborate with ML engineers to develop end-to-end solutions, optimize prompts, and ensure the scalability and reliability of AI applications.
The Tech Lead Data Scientist will define and oversee the technical methodology for evaluating, monitoring, and optimizing AI systems in production. They will provide hands-on guidance to program teams and mentor a team of data analysts to ensure rigorous validation and performance measurement.
The Senior Data Scientist leads the end-to-end lifecycle of AI and machine learning initiatives to improve healthcare outcomes and operational efficiency. They also mentor junior staff and translate complex technical findings into actionable insights for stakeholders.
You will develop, deploy, and optimize AI agents while integrating them with external systems to solve practical customer problems. Additionally, you will collaborate with customers and internal teams to gather requirements, conduct demos, and provide technical guidance for successful AI deployments.
The AI Engineer will design and deliver production AI systems across commercial and federal modernization programs. They are responsible for end-to-end development including data, model integration, services, UI, infrastructure, security, and deployment.
The Principal AI Developer Experience Architect will design and own the end-to-end architecture for AI-augmented development, ensuring enterprise-grade AI coding assistants are secure, performant, and reliable. They will lead the integration of AI-driven code review tools and establish global standards for AI-assisted workflows across the engineering organization.
Design and develop machine learning and deep learning models to solve complex engineering problems in the semiconductor space. Collaborate with multidisciplinary teams to deploy AI solutions and ensure model robustness through evaluation and monitoring.
You will travel to active jobsites to shadow project teams, capture expert knowledge, and identify opportunities for technology intervention. Additionally, you will coach field teams on adopting AI-assisted workflows and provide intelligence to inform product development.
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You will design, build, and operate LLM-powered agents that coordinate engineering workflows, surface project risks, and generate structured deliverables. Additionally, you will own the agent orchestration layer and collaborate with cross-functional teams to integrate AI features into hardware development platforms.
You will own the AI foundation by designing durable, production-grade generative AI systems that power patient engagement and clinical operations. This role involves defining system architecture, setting technical standards for safety and performance, and acting as a technical multiplier for the engineering team.
You will lead the design and delivery of agentic systems that orchestrate tools, data, and policies to solve complex business workflows. This involves building modular components, ensuring system reliability through observability, and collaborating with enterprise clients to translate business processes into production-ready AI solutions.
Lead multiple AI projects from kickoff to delivery while managing cross-functional teams and client expectations. Utilize AI tools to streamline project management tasks, track velocity, and ensure milestones are met within scope.
You will own the internal AI automation roadmap by identifying repetitive operational tasks and designing end-to-end automated workflows. This involves partnering with team leads to implement measurable solutions using LLM APIs and low-code platforms.
You will lead the strategy and development of AI platform capabilities that connect data, workflows, and integrations to power smarter agent experiences. This involves partnering with cross-functional teams to define product direction, prioritize high-value features, and ensure scalable, reliable AI system performance.
The Head of AI will define and execute a visionary AI roadmap while leading a multidisciplinary team to integrate cutting-edge research into health technology products. This role involves collaborating with senior leadership to align AI strategy with clinical priorities and ensuring the development of trustworthy, compliant AI solutions.
The Senior Security Engineer will own the end-to-end security of AI systems, including threat modeling, adversarial testing, and implementing security controls across the ML lifecycle. They will collaborate with cross-functional teams to embed security into AI development pipelines and ensure compliance with regulatory frameworks.
Lead strategic client engagements focused on data and AI modernization, governance, and practice data enablement within legal organizations. Serve as a trusted advisor to clients while mentoring junior team members and contributing to the firm's thought leadership.
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You will design and execute multi-turn conversational prompts to evaluate the personalization quality of AI models. Additionally, you will perform side-by-side comparisons of model responses and provide detailed rationales for your rankings.
The Lead Software Engineer will architect and develop scalable GenAI platforms and developer tooling while integrating LLMs into enterprise solutions. They will also mentor engineering peers and collaborate across teams to ensure the reliability and performance of AI-driven production systems.
Lead the transition to autonomous AI agents within engineering development cycles, including code generation, hardware simulation, and testing. Define the AI strategy, establish performance metrics, and implement governance frameworks to ensure safety and security in agentic workflows.
The specialist will audit, rewrite, and refine AI-generated content to ensure stylistic consistency and technical accuracy. Responsibilities include correcting LaTeX mathematical expressions and fixing markdown formatting errors.
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