Senior Engineer - AI-Powered Search

Experience

Salary

Expected Notice Period

Shift

Opportunity Type

Placement Type

(*Note: This is a requirement for one of Uplers' client - A US-based, venture-funded company transforming eCommerce visuals with AI-powered bulk photo editing)What do you need for this opportunity?Must have skills required:media transformation, proxy generation, video ingest, AI/MLOps, Database architecture, ECS (GPU), AWSA US-based, venture-funded company transforming eCommerce visuals with AI-powered bulk photo editing is Looking for:

What You’ll Own

• Architect the next-gen search and metadata foundation for Evolphin Zoom
• Lead integration of LLM-generated media embeddings into a unified semantic + similarity + structured search experience
• Collaborate with ML team to operationalize multimodal AI pipelines that extract visual, audio, and textual data from media assets — from face/logo detection to transcript embeddings to LLM driven media summaries
• Replace or refactor the current object store and metadata engine with a scalable, multi-tenant architecture in AWS
• Design APIs and storage schemas that support:
• Time-coded media tags (e.g., logos, faces, speech)
• Structured attributes (e.g., IPTC, EXIF, DRM usage rights, category etc)
• AI-derived embeddings and semantic summaries
• Drive the full lifecycle: schema design, vector indexing, embedding integration, and rollout to production
• Collaborate with AI/ML and UX teams to power real-time, intuitive media search
  

Skills & Experience

• Database Architecture, AI/MLOps, AWS (Required)
• Hands-on experience in DB design and architecture (2-3 yrs)
• Experience with designing asset metadata schemas in MAM/DAM-like systems
• Experience modeling object-style relationships (asset versions time-coded data)
• Experience with schema-less and structured stores like DynamoDB, PostgreSQL, or Aurora
• Familiarity with designing systems that support live and archived media (tiered storage)
• Hands-on experience with vector databases like Weaviate, Qdrant, FAISS, or Pinecone (2-3 yrs)
• Familiarity with hybrid search (structured filters + semantic vectors)
• Familiarity with performance tuning of semantic search (indexing, recall, latency, memory)
• Experience migrating from monolithic or tightly coupled Java systems to service-oriented designs
• Ability to work with AI outputs across multiple modalities — aligning embeddings, timestamps, and metadata for unified retrieval (2 years)
• Experience building services on ECS (GPU), Lambda, API Gateway, SQS/EventBridge (2-3 yrs)
• Experience with deploying scalable ML services on EC2, Fargate (2 yrs)
• Strong CI/CD practices (2-3 yrs)
• Comfort working across Java and Python boundaries when modernizing APIs or data models
  

Additional Skills & Experience

• Familiarity with video ingest, proxy generation, media transformation, and archive workflows in media pipelines
• Experience working with formats like MXF, ProRes, H.264, DNxHD
• Experience supporting React or Electron frontends with performant APIs
• Familiarity with designing data access patterns and query payloads to optimize media-heavy UIs
  

You Might Be a Fit If You…

• Care about making AI useful in real-world creative workflows
• Enjoy blending semantic models with structured metadata in production
• Know how to move fast in high-scale environments with tech debt
• Can bridge the gap between legacy Java systems and modern Vector-based search systems
  

How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
  

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