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Where Ambition Meets Innovation At LPLs Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. Were proud to be expanding and reaching new heights in Hyderabad. Join us as we create something extraordinary together. What if you could build a career where ambition meets innovation At LPL Financial Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. We are proud to expand in Hyderabad. Join us as we create something extraordinary together. Job Overview: The AVP, AI/ML Engineering, Tech Lead is a senior technical leader responsible for architecting, building, and operationalizing the AI systems that will transform LPLs advisor, client, and operational experiences. This role guides the engineering of LLM-driven applications, agentic AI systems, autonomous workflows, retrieval-augmented generation (RAG), and the enterprise Knowledge Graph, all built within a scalable, governed, cloud-native environment. Operating within the AI/ML Engineering organization, this leader sets technical direction for how LPL builds production-quality AIleveraging AWS Bedrock, generative model ecosystems, and modern ML tooling. Responsibilities: AI/ML Architecture & Agentic System Design: Architect and lead implementation of agentic AI systems capable of multi-step reasoning, tool use, workflow orchestration, and domain-specific autonomy. Build LLM-based agents that interact with APIs, data products, and enterprise systems to drive intelligent automation and decision support. Design orchestration layers that incorporate memory, context management, and dynamic planning for advanced agent behaviours. Develop RAG architectures that integrate embeddings, vector search, and semantic retrieval into agentic workflowsAWS BedrockDriven AI Platform Engineering: Lead adoption of AWS Bedrock for model selection, orchestration, governance, and enterprise scaling of LLMs and generative AI. Implement Bedrock Features such as: Guardrails, Model evaluation, Provisioned throughput, Custom model fine-tuning. Integrate Bedrock models with downstream systems, including microservices, pipelines, and agent frameworks. Partner with enterprise architecture to define standards for Bedrock usage and model lifecycle governance.Knowledge Graph & Semantic Intelligence: Own engineering and operationalization of the Enterprise Knowledge Graph and integrate it with LLM and agent frameworks as a structured reasoning layer, Implement ontology-driven enrichment, entity resolution, and graph-based retrieval for AI capabilities, Connect graph services to agents to support semantic grounding, consistency, and contextual awarenessML Engineering & MLOps: Build and maintain automated pipelines for training, evaluation, deployment, and monitoring of traditional and generative ML models, Establish rigorous MLOps standards including CI/CD, reproducibility, drift detection, and quality gates, Develop feature stores, vector databases, and real-time inference services optimized for AI workloads.Engineering Delivery & Cross-Team Collaboration: Partner deeply with DIME to ensure upstream data pipelines meet enterprise AI-grade requirements for freshness, quality, lineage, and metadata, Work with Lakehouse Engineering and Data Product teams to align models, features, and data availability, Collaborate with governance and security teams to enforce responsible AI practices and regulatory controls What are we looking for Were looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work. Requirements: 8+ years of software engineering experience, with 3+ years in hands-on leadership of ML/AI initiatives. Should have leadership in designing and deploying machine learning systems in production.Full-stack proficiency building agentic/chat web experiences with Angular + TypeScript on the front end and Python (Flask/FastAPI) services on the back end. Experience implementing real-time/streaming UIs for LLM chat (e.g., server-sent events / streamed token responses) with responsive state .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.