Applied AI Solution Engineer

Job description

**Applied AI Solution Engineer** Location- Bangalore ( Hybrid ) Role- Full Time Experience 5+ years **About Kearney Activate** Kearney Activate is the business acceleration and enablement arm of Kearney Management Consulting, helping clients accelerate transformation through Data and AI.Our Data & AI business line bridges strategy, technology, and execution to deliver data-driven, AI-enabled,and cloud-powered transformations. We partner with leading platforms such as Microsoft, AWS,Snowflake, Databricks and Nvidia to help organizations build trusted data foundations, deploy intelligent solutions, and scale innovation. **We operate across four key focus areas:** - Advisory & Assurance Services: Driving strategy, governance, and change management to ensure impactful transformation. - Data Services: Building trusted, high-quality data foundations using Agentic AI across engineering, architecture, and governance. Insights Services: Unlocking value through analytics, data science, and AI-driven storytelling for better decision-making. - Asset Services: Developing reusable GenAI assets, accelerators, and AI-powered solutions that push the boundaries of innovation. **About the Role** As a Junior Applied AI Solution Engineer, you will work as part of a cross-functional teams to design, build, and deploy AI-driven applications. Particularly those leveraging Large Language Models (LLMs),agents, and other modern AI techniques. This is a hands-on, client-facing engineering role where you will contribute to solution design, development, testing, and delivery. You will work closely with senior engineers and client teams, gaining exposure to real-world AI solution engineering. As you grow in the role, you will take on more ownership, from end-to-end delivery to advising clients on AI opportunities and best practices. Our teams primarily build solutions using existing foundational models (closed and open-source), sometimes with light fine-tuning. We do not train foundational models from scratch. **Key Responsibilities** 1. AI Solution Development - Assist in building AI-powered applications, especially those involving LLMs, retrieval-augmented generation, and autonomous agents. - Support the design and hands-on implementation of end-to-end solutions with guidance from senior engineers. - Help integrate AI components into microservices, APIs, and client-facing applications. 2. Agent & Workflow Development - Support the creation of multi-step reasoning agents using LangGraph or LangChain Agents. - Implement tool-calling, memory, and routing logic. - Evaluate agent behaviour and improve reliability over iterations. 3. Client Collaboration & Communication - Participate in conversations with clients to understand their challenges, use cases, and requirements. - Help translate business needs into feasible AI/ML solution components. - Support senior engineers in presenting solution concepts and explaining AI capabilities in accessible terms. 4. Technical Problem-Solving - Contribute to solving technical issues encountered during development and implementation. - Identify blockers and escalate complex challenges to senior engineers and R&D teams. - Assist in documenting learnings and reusable patterns for future solutions. 5. Cross-Functional Collaboration - Work collaboratively with technical and non-technical teams to ensure solutions are aligned with client expectations. - Provide input into product, data, and design discussions where AI components a\\ect the user experience. 6. Continuous Learning & Growth - Stay informed about new models, AI tools, frameworks, and best practices. - Proactively learn new technologies and explore innovative ways to apply AI to real-world problems. - Experiment with new open-source models, orchestration frameworks, and retrieval techniques. - Share findings and help improve team practices. **Technical Skills** You have hands-on experience or strong foundational knowledge in several of the following areas: - Python programming and data technologies (SQL, APIs, basic data pipelines). - Building small-scale microservices or automation scripts. - Working with LLM APIs (Azure OpenAI, Gemini, Hugging Face, Anthropic). - Exposure to GenAI concepts (prompting, embeddings, finetuning, vector search) - Basic experience with RAG development and knowledge retrieval - Understanding of model fine-tuning, vector stores, embeddings concepts. - Ability to design simple solution architectures using Python & cloud tools. - Strong analytical and problem-solving mindset. - Willingness to learn new technologies when the project requires it. **Client-Facing Mindset** Even at a junior level, you should feel comfortable and excited to grow in these areas: - Communicating clearly with non-technical audiences. - Translating business needs into technical requirements (with support). - Understanding business processes and identifying where AI can add value. **Personal Traits** - Adaptable & Self-Motivated: You are proactive, resourceful, and able to operate independently with minimal handholding. - Passionate & Positive: You are genuinely excited about AI and its potential to transform industries. - Creative & Curious: You keep up with AI developments and enjoy experimenting with new toolsand methods. - Ethical & Responsible: You understand the importance of safe, fair, and responsible AI implementation. **Nice to Have** - Experience with building data pipelines, preparing and indexing datasets, and implementing embedding generation and retrieval evaluation for GenAI applications - Exposure to MLOps, Docker, CI/CD, or cloud platforms (AWS/GCP/Azure) - Hands-on work with agent workflows or multi-step reasoning models - Vibe coding skills - fast prototyping, AI-assisted development, improvisational coding **What We Offer** - Direct experience building real-world AI applications. - Mentorship from experienced AI