Engineering Lead - AI-Driven MAM

Experience

: 6.00 + years

Salary

: INR 6000000-6500000 / year (based on experience)

Expected Notice Period

: 15 Days

Shift

: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type

: Remote

Placement Type

: Full Time Permanent position(Payroll and Compliance to be managed by: Crop.Photo)

(*Note: This is a requirement for one of Uplers' client - Crop.Photo)What do you need for this opportunity?Must have skills required:MLOps, Python, Scalability, VectorDBs, FAISS, Pinecone/ Weaviate/ FAISS/ ChromaDB, Elasticsearch, Open searchCrop.Photo is Looking for:

Technical Lead for Evolphin AI-Driven MAM

At Evolphin, we build powerful media asset management solutions used by some of the world’s largest broadcasters, creative agencies, and global brands. Our flagship platform, Zoom, helps teams manage high-volume media workflows—from ingest to archive—with precision, performance, and AI-powered search.We’re now entering a major modernization phase, and we’re looking for an exceptional Technical Lead to own and drive the next-generation database layer powering Evolphin Zoom. This is a rare opportunity to take a critical backend system that serves high-throughput media operations and evolve it to meet the scale, speed, and intelligence today’s content teams demand.What you’ll ownLeading the re-architecture of Zoom’s database foundation with a focus on scalability, query performance, and vector-based search supportReplacing or refactoring our current in-house object store and metadata database to a modern, high-performance elastic solutionCollaborating closely with our core platform engineers and AI/search teams to ensure seamless integration and zero disruption to existing media workflowsDesigning an extensible system that supports object-style relationships across millions of assets, including LLM-generated digital asset summaries, time-coded video metadata, AI generated tags, and semantic vectorsDriving end-to-end implementation: schema design, migration tooling, performance benchmarking, and production rollout—all with aggressive timelines

Skills & Experience We Expect

We’re looking for candidates with 7–10 years of hands-on engineering experience, including 3+ years in a technical leadership role. Your experience should span the following core areas:System Design & Architecture(3–4 yrs)Strong hands-on experience with the Java/JVM stack (GC tuning), Python in production environmentsLed system-level design for scalable, modular AWS microservices architecturesDesigned high-throughput, low-latency media pipelines capable of scaling to billions of media recordsFamiliar with multitenant SaaS patterns, service decomposition, and elastic scale-out/in modelsDeep understanding of infrastructure observability, failure handling, and graceful degradationDatabase & Metadata Layer Design(3–5 yrs)Experience redesigning or implementing object-style metadata stores used in MAM/DAM systemsStrong grasp of schema-less models for asset relationships, time-coded metadata, and versioned updatesPractical experience with DynamoDB, Aurora, PostgreSQL, or similar high-scale databasesComfortable evaluating trade-offs between memory, query latency, and write throughputSemantic Search & Vectors(1–3 yrs)Implemented vector search using systems like Weaviate, Pinecone, Qdrant, or FaissAble to design hybrid (structured + semantic) search pipelines for similarity and natural language use casesExperience tuning vector indexers for performance, memory footprint, and recallFamiliar with the basics of embedding generation pipelines and how they are used for semantic search and similarity-based retrievalWorked with MLOps teams to deploy ML inference services (e.g., FastAPI/Docker + GPU-based EC2 or SageMaker endpoints)Understands the limitations of recognition models (e.g., OCR, face/object detection, logo recognition), even if not directly building themMedia Asset Workflow(2–4 yrs)Deep familiarity with broadcast and OTT formats: MXF, IMF, DNxHD, ProRes, H.264, HEVCUnderstanding of proxy workflows in video post-productionExperience with digital asset lifecycle: ingest, AI metadata enrichment, media transformation, S3 cloud archivingHands-on experience working with time-coded metadata (e.g., subtitles, AI tags, shot changes) management in media archivesCloud-Native Architecture (AWS)(3–5 yrs)Strong hands-on experience with ECS, Fargate, Lambda, S3, DynamoDB, Aurora, SQS, EventBridgeExperience building serverless or service-based compute models for elastic scalingFamiliarity with managing multi-region deployments, failover, and IAM configurationBuilt cloud-native CI/CD deployment pipelines with event-driven microservices and queue-based workflowsFrontend Collaboration & React App Integration(2–3 yrs)Worked closely with React-based frontend teams, especially on desktop-style web applicationsFamiliar with component-based design systems, REST/GraphQL API integration, and optimizing media-heavy UI workflowsAble to guide frontend teams on data modeling, caching, and efficient rendering of large asset librariesExperience with Electron for desktop apps

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