Senior ML Engineer

About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of successful migrations and implementations across AWS and Salesforce, AllCloud helps businesses remain at the forefront of innovation through AI-led professional and managed services. As an AWS Premier and audited managed services Partner, AllCloud provides comprehensive cloud journey support, from migration and modernization to AI adoption and ongoing management. AllCloud serves clients globally across EMEA and North America. Role Overview We are seeking a Senior Machine Learning Engineer – AI & GenAI (AWS) to deliver client-facing AI and Generative AI engagements on AWS. This is a hands-on consulting and delivery role where you will own technical workstreams end to end and work directly with client architects, stakeholders, data scientists, ML engineers, and solution architects across the US and India. You will contribute across discovery and assessments, proof-of-concepts, AWS Migration Acceleration Program (MAP) engagements, production hardening, and staff augmentation. The role requires strong technical depth, excellent communication, independent decision-making, and the ability to operate effectively in ambiguous, fast-paced environments. Key Responsibilities Design, build, and deploy Generative AI applications on Amazon Bedrock, including RAG, document intelligence, summarisation, classification, and assistant-style solutions. Architect and implement agentic and multi-agent systems using Bedrock, AWS Step Functions, Lambda, and related AWS services. Build retrieval solutions using embeddings, hybrid keyword/vector search, Amazon OpenSearch, pgvector, relevance tuning, reranking, citation, and grounding. Own the Amazon SageMaker lifecycle, including feature engineering, training, tuning, model registry, deployment, MLOps, retraining, drift detection, and CI/CD. Build AI evaluation and observability frameworks using golden datasets, LLM-as-judge, regression testing, error taxonomies, tracing, and human-in-the-loop workflows. Implement AI guardrails, prompt-injection defences, PII handling, and responsible AI controls for regulated and data-sensitive use cases. Build and integrate AI data foundations using S3, Glue, Spark, Redshift, Athena, DMS, Kinesis, and Firehose. Design secure, multi-tenant, scalable, and cost-aware AWS architectures using IAM, Lake Formation, VPC, PrivateLink, API Gateway, EKS, ECS, Fargate, and Lambda. Define infrastructure as code using AWS CDK, CloudFormation, or Terraform and deliver through CI/CD. Estimate, monitor, and optimise AI and data workload costs, including model selection, token economics, caching, compute, and storage. Lead technical discovery, requirements sessions, architecture discussions, client presentations, and executive readouts. Contribute through code reviews, reusable accelerators, internal engineering patterns, and mentoring of engineers. Requirements Technical Requirements Generative AI & AWS: Hands-on experience with Amazon Bedrock, foundation models, RAG, Knowledge Bases, Guardrails, function calling, LLM gateways, and model selection based on quality, latency, cost, and data residency. Agentic AI: Experience building tool-using and multi-agent systems using Bedrock, Step Functions, Lambda, Bedrock Agents/AgentCore, Strands Agents SDK, and MCP. Retrieval & Search: Strong experience with Amazon OpenSearch, vector/hybrid search, embeddings, BM25, reranking, pgvector, and end-to-end RAG including document parsing, chunking, grounding, and retrieval evaluation. SageMaker & MLOps: Experience with SageMaker training, tuning, deployment, Model Registry, Pipelines, monitoring, drift detection, CI/CD, and fine-tuning open-weight models including LoRA. AI Evaluation & Responsible AI: Experience with golden datasets, LLM-as-judge, regression testing, prompt engineering, guardrails, prompt-injection protection, PII handling, human-in-the-loop workflows, and LLM observability. AWS Data Stack: Strong knowledge of S3, Glue, Spark, Redshift, Athena, DMS, Kinesis, Firehose, Aurora PostgreSQL, DynamoDB, QuickSight, and lakehouse technologies such as Iceberg/Delta Lake. AWS Architecture & Security: Experience with IAM, Lake Formation, VPC, PrivateLink, EKS/ECS/Fargate, Lambda, API Gateway, infrastructure as code, CI/CD, monitoring, and cost optimisation. Programming & Engineering: Expert Python with pandas, NumPy, scikit-learn, PyTorch, boto3, and pytest; strong SQL, Git, Docker, and Linux skills, with FastAPI/backend and Streamlit/React familiarity. Classical ML & NLP: Strong foundation in statistics, supervised/unsupervised ML, time-series forecasting, demand/supply planning, and NLP including classification, entity extraction, topic modelling, and free-text analysis. Requirements Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Mathematics, a related quantitative field, or equivalent demonstrated experience. 5–8+ years of experience in machine learning engineering, data science, or AI engineering, including at least 3 years of hands-on AWS experience. At least 1 year of production-grade Generative AI/LLM experience, including a system taken to production or a client-accepted POC that you can explain in architectural detail. Demonstrated experience working directly with external clients or business stakeholders and independently leading technical sessions. Experience working effectively across significant time-zone differences with strong self-direction and ownership. Excellent written and spoken English, with the ability to create clear architecture documentation and present to senior audiences. AWS Certified Machine Learning and Anthropic certification, held or committed to within 60 days of joining, as applicable. Preferred Qualifications AWS Certified Solutions Architect – Professional. Consulting, professional services, systems integrator, or AWS Partner experience, with familiarity with utilisation, scoping, and statement-of-work realities. Experience in healthcare/life sciences, financial services, insurance, manufacturing/supply chain, or compliance/legal technology. Experience with LangChain, LangGraph, LlamaIndex, CrewAI, Strands Agents SDK, Bedrock AgentCore, or MCP server development. Experience with knowledge graphs, Amazon Neptune, GraphRAG, Snowflake, dbt, Apache Airflow, Databricks, Amazon Connect, or contact-centre AI. Open-source contributions, publications, conference talks, AWS Community Builder, or AWS Hero experience. Certification Requirements AWS Certified Machine Learning — Specialty or current designated AWS ML certification: Required (at hire or within 60 days) Anthropic certification: Preferred (at hire or within 60 days) AWS Certified Solutions Architect – Professional: Strongly Preferred AWS Certified Data Engineer – Associate, AWS Certified AI Practitioner, AWS Certified Security – Specialty: Advantageous Signs You May Be a Great Fit Impact: Play a pivotal role in shaping a rapidly growing venture studio with Cloud-driven digital transformation. Culture: Thrive in a collaborative, innovative environment that values creativity, ownership, and agility. Growth: Access professional development opportunities, and mentorship from experienced peers. Benefits: Competitive salary, wellness packages, and flexible work arrangements that support your lifestyle and goals.