Advisor Software Engineer (AI/ML)

Salary: $86000 - 134000 per year Requirements: We require at least 6 years of hands-on software engineering experience building, developing, and supporting scalable enterprise applications and cloud-native solutions. We need strong Python expertise, including backend services, APIs, automation, data-processing workflows, and production-grade AI/ML applications. We expect solid system design and architecture skills for scalable, resilient, secure, and maintainable solutions. We require experience creating API-led solutions, such as REST APIs, microservices, service orchestration, secure APIs, and enterprise integrations. We need practical AWS cloud development experience, including serverless, event-driven, containerized, and distributed application patterns. We expect experience with SQL and data platforms such as PostgreSQL, Snowflake, or comparable relational and analytical databases. We require a strong grasp of the software development lifecycle, including requirements analysis, design, development, testing, deployment, production support, and maintenance. We need experience applying engineering best practices, including secure coding, code reviews, automated testing, CI/CD, observability, performance tuning, and production issue resolution. We expect experience working with technical and business partners, translating business needs into technical solutions, and communicating risks, trade-offs, and delivery impacts. A bachelors or masters degree in Computer Science, Engineering, Information Technology, Data Science, Machine Learning, Artificial Intelligence, or a related discipline is preferred. We value experience delivering AI-enabled enterprise software, including GenAI applications, intelligent automation, AI-assisted workflows, and AI-driven decision support. We value experience with MLOps, vector databases, embedding-based search, MCP-based tool integration, and enterprise AI governance. We value experience preparing technical papers, invention disclosures, patent-supporting documentation, or reusable engineering playbooks. We value testing expertise across unit, integration, functional, regression, and performance testing. We value experience with Scaled Agile Framework, Agile delivery, cybersecurity vulnerability remediation, and enterprise delivery practices. We value strong relationship management skills and the ability to collaborate across stakeholders, influence outcomes, and support strategic technology initiatives. We need hands-on AWS engineering experience using AWS service APIs, AWS CLI, AWS SDKs, and cloud-native deployment patterns. We need familiarity with core AWS services such as Lambda, S3, EC2, API Gateway, IAM, CloudWatch, EventBridge, SQS, SNS, and Step Functions. We value experience with AWS AI/ML services such as SageMaker and Bedrock, as well as AWS-based model deployment and inference patterns. We value container and DevOps experience, including Docker, Kubernetes, ECS/EKS, CI/CD pipelines, automated testing, and release management. We need understanding of cloud security and compliance practices, including IAM, encryption, secrets management, vulnerability remediation, logging, and secure design. We need hands-on experience in machine learning, AI engineering, data science, or applied AI solution development. We value experience with Generative AI and large language models, including OpenAI, Anthropic, Cohere, Amazon Bedrock, or similar enterprise platforms. We need strong Python skills and familiarity with AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, and related frameworks. We value experience building retrieval-augmented generation solutions, including embeddings, vector databases, semantic search, document retrieval, chunking, prompt grounding, and response evaluation. We value experience with LLM application patterns, including prompt engineering, guardrails, model evaluation, tool/function calling, agentic workflows, and responsible AI. We value familiarity with frameworks and tools such as LangChain, LlamaIndex, FastAPI, MCP tools, vector databases, and API-based AI service integration. We value experience with model development and deployment, including feature engineering, model serving, monitoring, MLOps, and productionizing AI/ML capabilities. We require proven technical leadership in software engineering teams, including solution direction, task assignment, progress tracking, issue resolution, and delivery accountability. We need experience mentoring and coaching engineers through technical guidance, code review feedback, design support, and career development. We value the ability to influence technical decisions and engineering practices, including architecture discussions, trade-offs, quality improvements, and modern AI/ML and cloud standards. We need experience partnering with product owners, architects, business stakeholders, risk, security, and operations teams to deliver aligned solutions. We value the ability to create hi