Here is the lay out of an end-to-end Media & Entertainment reference workflow on AWS

Map each bullet from the role description to specific AWS services and patterns

Show where AI/ML & VLMs live in that picture

1. End-to-end M&E reference workflow on AWS

Think in layers. A typical AWS M&E architecture looks like this:

A. Ingest & Acquisition

  • Sources: cameras, encoders, edit suites, legacy MAM/DAM, 3rd-party distributors
  • AWS services:
    • S3 / S3 Multi-Region Access Points for file ingest and raw mezzanines
    • AWS Elemental MediaConnect for live contribution feeds (ST 2110 → cloud, TS over IP, etc.) Amazon Web Services, Inc.+1
    • Kinesis Video Streams for camera/IoT style video streams Amazon Web Services, Inc.
    • Optional: DataSync / Transfer Family for moving from on-prem NAS/object into S3

B. Media Lake & Data Lake

  • Media lake: S3://media-landing/raw | mezzanine | proxy | renditions | thumbnails
  • Data lake: S3://data-lake/events | analytics | ML-features with Glue Data Catalog, Lake Formationfor governance. Amazon Web Services, Inc.+1
  • This is where MAM/DAM and downstream tools “see” assets.

C. Media Processing (Video / Audio / Images / Text)

  • Transcoding & packaging (VOD & Live):
    • AWS Elemental MediaConvert – file-based VOD transcode, ABR ladders, captions, DRM prep
    • MediaLive – live encoding from MediaConnect or on-prem SDI → OTT/broadcast outputs
    • MediaPackage – HLS/DASH/CMAF packaging & origin for OTT; integrates with CloudFront CDN Amazon Web Services, Inc.+1
    • MediaTailor – SSAI / channel assembly for personalized ads / FAST channels Amazon Web Services, Inc.
  • Image & thumbnail processing:
    • Lambda + S3 events to auto-generate thumbnails, posters, sprites
    • Optional AWS Batch / Fargate containers for heavy batch image jobs
  • Audio processing:
    • Amazon Transcribe – speech-to-text from audio/video
    • Amazon Polly – TTS (e.g., localized voiceovers)
    • Custom audio ML in SageMaker for loudness/QC or music tagging LogicMonitor
  • Text engineering:
    • Amazon Comprehend – entity extraction, keyphrases, sentiment on reviews, comments, transcripts LogicMonitor
    • LLMs via Amazon Bedrock for summarization, title/description generation, localization, SEO copy

D. AI/ML & Video Language Models (VLMs)

  • Classical CV / media analysis:
  • Multimodal / VLM style models:
    • TwelveLabs models in Bedrock – multimodal video understanding & video language model (search, classify, summarize, describe) Amazon Web Services, Inc.+1
    • Amazon Nova & Titan multimodal embeddings in Bedrock – shared embedding space for text, image, video, audio, enabling semantic search across all modalities Amazon Web Services, Inc.+2AWS Documentation+2
  • Custom ML:
    • SageMaker – training fine-tuned classifiers (e.g. sports highlight classifier, brand safety scoring, promo effectiveness)

E. Orchestration & Application Layer

  • Workflow orchestration:
    • Step Functions, EventBridge, Lambda, sometimes Airflow on MWAA for complex pipelines
    • Pattern: S3 put → EventBridge → Step Functions → MediaConvert + Rekognition + Bedrock chain
  • Application / microservices:
    • EKS (Kubernetes) or ECS/Fargate for REST/gRPC microservices
    • API Gateway or AppSync (GraphQL) as API front door
    • Cognito / external IdP for auth

F. Delivery & Experience

  • CloudFront as primary CDN, backed by MediaPackage or S3/MediaStore origins Amazon Web Services, Inc.+1
  • Social outputs (YouTube, TikTok, etc.) via integration services (Lambda + API, Mulesoft, etc.)
  • Client apps: Web, mobile, CTV, STB, broadcast playout, FAST.

G. Analytics, Campaigns & Data Products

  • Ingestion: Kinesis / MSK / Firehose → S3
  • Processing: Glue / EMR / Databricks (on AWS) / Snowflake
  • Query/BI: Athena, Redshift, QuickSight for dashboards and for “data-driven campaigns.”

This is your “visual canvas.” Every bullet in the job description plugs into one or more slices of this diagram.

2. Mapping the responsibilities to AWS architectures

a) Translate Conceptual → Technical Architecture

Example: Business asks

Auto-tag all incoming content, generate social highlights, and feed a personalization engine.

The map will be:

  1. Ingest: S3 (file) or MediaConnect/MediaLive (live)
  2. Core workflow:
    • MediaConvert → mezzanine & ABR ladder
    • Rekognition Video → scene/people/action tags
    • Transcribe + Comprehend → transcripts + NLP tags
    • VLM via Bedrock (TwelveLabs / Nova) → highlight summaries, title & description drafts
  3. Storage & governance:
    • Tags and ML output to Glue-catalogued tables in S3, surfaced via MAM/DAM
  4. API & consumption:
    • API Gateway + Lambda/EKS microservices for search, recommendation, content discovery
  5. Non-functional:
    • Multi-AZ, encryption (KMS), IAM roles/permissions, logging/metrics (CloudWatch, OpenTelemetry)

That’s exactly “converting logical architecture into a concrete tech architecture leveraging cloud services and best practices.”

b) Domain-specific M&E workflows on AWS

Workflow 1 – VOD onboarding & enrichment

  • Visual flow:
    On-prem MAM → S3 (raw) → Lambda event → Step Functions →
    MediaConvert job → Rekognition Video & Bedrock VLM → S3 (renditions + metadata) →
    Athena / OpenSearch for search → MediaPackage/CloudFront for playback.
  • Apps involved: MAM dashboard, QC UI, metadata tagging UI, search portal, OTT apps.

Workflow 2 – Live streaming with AI QC & SSAI

  • Visual flow:
    On-prem encoder → MediaConnect → MediaLive → MediaPackage → CloudFront
    Parallel:
    MediaLive → Kinesis Video Streams → Rekognition Video for real-time detection & QC
    Ad decisioning via MediaTailor + 3rd-party ad server; event logs into Kinesis → S3 → analytics.

This addresses live video engineering, ABR, HLS/DASH, and ad-supported FAST channels.

Workflow 3 – Media lake + VLM-powered editorial/search

  • Visual flow:
    Edit exports to S3 → metadata + EDLs stored in DynamoDB / RDS →
    Batch jobs call Bedrock VLM (TwelveLabs / Nova) to:
    • Summarize episodes
    • Tag scenes (e.g., “goal highlight”, “car chase”, “cooking segment”)
    • Generate synopsis, localized descriptions
  • Embeddings go into OpenSearch or Aurora for semantic search:
    “Show me all scenes where the host is cooking outdoors at night” → search vectors + filters.

This is the “Video Language Model” piece in a very practical, client-friendly way.

c) Performance Optimization / Engineering

Concrete knobs on AWS:

  • MediaConvert/MediaLive:
  • AI/ML workloads:
    • Use SageMaker instance types (GPU/Inferentia) and endpoint autoscaling
    • Cache inference results (e.g. via DynamoDB/ElastiCache) for re-used content
    • Use batch transforms for non-realtime analysis to exploit spot instances
  • Microservices:
    • Right-size containers (EKS/ECS/Fargate), adopt async patterns (SQS/SNS, Kinesis)
    • Use CloudFront & MediaPackage origin shielding to reduce origin load

You can talk about profiling, load testing (Locust, JMeter, k6), replaying logs via Kinesis, and capacity planning.

d) Technology Stack Implementation & Governance

Your “standard stack” for this role might be:

  • Core infra:
    • VPC, subnets, Transit Gateway, IAM, KMS, Secrets Manager
  • Compute: EKS for core services, plus Lambda/Fargate for glue logic
  • Media: MediaConvert, MediaLive, MediaPackage, MediaConnect, MediaTailor, Kinesis Video Streams
  • Storage/Data: S3 + Glacier, DynamoDB, Aurora/Postgres, S3-based data lake
  • AI/ML: Rekognition, Transcribe, Comprehend, Bedrock (Nova/TwelveLabs/Titan), SageMaker
  • Orchestration: Step Functions, EventBridge, MWAA
  • Observability: CloudWatch, X-Ray, OpenSearch, third-party APM
  • IaC & CI/CD: CloudFormation/CDK or Terraform; CodePipeline/CodeBuild/CodeDeploy or GitHub Actions

“Define and govern the technology stack” means you publish reference architectures, golden patterns, and templates—then enforce via IaC and guardrails (e.g., Control Tower, SCPs, config rules).

e) Client Collaboration & Technical Leadership

On AWS this looks like:

  • Discovery workshops:
    • Map current on-prem tools (e.g., Vizrt, EVS, legacy transcoders, MAM) to cloud equivalents or integrations.
    • Draw “as-is” vs “to-be” diagrams: data flows, metadata handoffs, operational roles.
  • Iteration:
    • Start with a thin vertical slice: e.g., one show or channel → move only its ingest + AI tagging to the cloud → measure cost/benefit.
    • Use POCs to de-risk: sample pipeline with a handful of titles or a single live event.
  • Leadership:
    • Code reviews & design reviews for teams building microservices, ML workflows, and IaC.
    • Patterns like blue-green deploys, canary releases, feature flags, and rollbacks via CI/CD.

f) Technology Evaluation (COTS / 3rd party)

Concrete steps:

  • Compare AWS-native vs COTS (Telestream, Dalet, Vizrt, SDVI, Imagen, etc.) for each domain:
    • Cost model (license vs consumption)
    • Integration with S3, IAM, CloudTrail, etc.
    • Vendor’s M&E-specific features vs building on primitives.
  • Re-use existing client tech:
    • If client already has Snowflake or Databricks, you integrate them into S3-based lakehouse rather than forcing Redshift. Amazon Web Services, Inc.

You’ll position yourself as the person who knows when to build vs buy vs reuse.

g) Prototyping & Documentation

You’d typically produce:

  • Architecture diagrams:
    • “Ingest & Media Lake”
    • “VOD Enrichment Pipeline with Rekognition & Bedrock”
    • “Live SSAI Workflow with MediaLive/MediaPackage/MediaTailor”
  • Design specs:
    • Sequence diagrams per workflow (e.g. S3 event → Step Functions → MediaConvert → Rekognition → Glue)
    • Non-functional requirements: RPO/RTO, latency targets, SLOs, cost budgets

Tools: draw.io, Lucidchart, PlantUML, or AWS Architecture Icons in PowerPoint/Keynote.

3. How the “Required Skills” show up on AWS

AI/ML in Media

Cloud/Data Architecture

  • Build event-driven microservices (EKS/ECS + EventBridge/SQS/Kinesis + API Gateway)
  • Implement media lake on S3 + data lakehouse on S3 + Glue + Lake Formation + Snowflake/Databricks/Redshift. Amazon Web Services, Inc.+2Amazon Web Services, Inc.+2
  • Use CDC tools (DMS, Debezium on MSK) to sync on-prem databases into cloud data lake.

Video/Audio Engineering

Software Engineering Foundation

  • Python/Node/Java microservices implementing:
    • Ingest APIs, ML orchestration, QC tooling, editorial UIs
    • CI/CD via CodePipeline or GitHub Actions; IaC via CDK/Terraform

M&E Domain Expertise

Map content supply chain concepts (ingest → edit → MAM → archive → distribution → analytics) into AWS primitives and partner solutions.