Mid AI Machine Learning Engineer

Sprout Solutions; Mandaluyong; On-site; PHP 900,000 - 1,300,000; Full time

Job summary

Sprout Solutions is seeking an AI/ML Engineer to design, build, and deploy intelligent systems and AI agents across products. You will implement agentic workflows, orchestration pipelines, and model integrations using LangChain, LangGraph, and other toolkits.

Expect collaboration with senior engineers to ensure reliable, scalable AI components that align with responsible AI practices. The role requires strong Python, FastAPI experience, and hands-on work with API design, deployment, and MLOps.

Qualifications

• Bachelor's degree preferred in CS/AI/Engineering or related field.
• 2–4 years in AI engineering or related fields, with applied AI/workflow automation experience.
• Hands-on experience building or deploying AI agents, chatbots, or intelligent workflows using LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.
• Strong Python proficiency including FastAPI and modern SDLC practices (version control, testing, CI/CD).
• Experience with API design and integration (REST, gRPC, GraphQL).
• Understanding of ML concepts: model fine-tuning, embeddings, evaluation, prompt engineering, LLM ops, RAG.
• Familiarity with vector DBs (Qdrant, Pinecone, Weaviate) and cloud AI services (Azure/AWS/GCP).
• DevOps/MLOps practices, CI/CD, telemetry, environment automation, production deployment.
• Containerization/orchestration (Docker, Kubernetes) knowledge is a plus.
• Strong problem-solving and cross-functional collaboration skills.
• Awareness of Responsible AI principles and governance.

Responsibilities

• Develop and deploy AI agents and workflow-based systems that autonomously reason and decide.
• Build AI workflows using LangChain/LangGraph and similar tools for tool use, memory, and context understanding.
• Integrate agents with internal/external APIs, databases, and third-party tools for intelligent automation.
• Create and maintain API wrappers/connectors for enterprise systems and external services.
• Collaborate to modernize APIs for AI interoperability, observability, and security.
• Assist in implementing Model Context Protocol (MCP) or similar standards for agent-system interaction.
• Fine-tune/adapt ML/foundation models for use cases and deploy within the pipeline.
• Support AI-centric DevOps/MLOps and CI/CD for model services and telemetry.
• Monitor and improve deployed AI systems with feedback loops and observability metrics.
• Follow Responsible AI guidelines and maintain production-grade codebases.

Skills

Python

LangChain

LangGraph

CI/CD

API design

LLM operations

RAG concepts

Vector databases

Docker

Kubernetes

Azure

AWS

GCP

Git

APIs (REST/GRPC/GraphQL)

Education

Bachelor's degree

Tools

Docker

Kubernetes

Azure

AWS

GCP

Git

Job description

Job Description:

Main Area Of Responsibility

As an AI/ML Engineer, you will help design, build, and deploy intelligent systems and AI agents that power next-generation experiences across our products. You will work closely with senior engineers and cross-functional teams to implement agentic workflows, orchestration pipelines, and model integrations using frameworks such as LangChain, LangGraph, and other emerging AI toolkits.

Your role will involve developing AI agents, API wrappers, and microservices that connect various systems, enabling contextual reasoning and automation. You will also assist in fine-tuning and deploying machine learning or foundation models where applicable and ensure that AI components are reliable, scalable, and aligned with responsible AI principles.

The AI Chapter owns all AI-specific deployment, observability, and lifecycle operations, and as part of this team, you will support efforts to maintain these pipelines, modernize APIs for AI consumption, and, where required, help implement Model Context Protocol (MCP) or similar interoperability layers to enhance agent-to-system communication.

Responsibilities

• Contribute to the development and deployment of AI agents and workflow-based systems that autonomously perform reasoning and decision-making tasks.
• Implement and maintain AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar, enabling tool use, memory, and contextual understanding.
• Integrate agents with internal and external APIs, databases, and third-party tools to enable intelligent automation and information retrieval.
• Assist in the development and maintenance of API wrappers or connectors that allow agents to interact with enterprise systems and external services.
• Collaborate with platform and engineering teams to modernize and document APIs, ensuring they are optimized for AI agent interoperability, observability, and security.
• Support the design or implementation of Model Context Protocol (MCP) or similar standards to facilitate seamless interaction between agents and systems.
• Fine‑tune or adapt custom ML or foundation models for specific use cases and deploy them as part of the agentic pipeline when necessary.
• Support AI‑centric DevOps and MLOps workflows, including CI/CD for model services, environment configuration, versioning, and telemetry integration.
• Participate in the monitoring, evaluation, and continuous improvement of deployed AI systems through feedback loops and observability metrics.
• Follow responsible AI guidelines, ensuring fairness, transparency, explainability, and safety in all implementations.
• Collaborate with senior engineers to document designs, improve internal AI frameworks, and maintain clean, production-ready codebases.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field is preferred.
• 2–4 years of experience in AI engineering, software development, intelligent systems, or related fields, preferably involving applied AI, workflow automation, or production‑grade solutions.
• Hands‑on experience building, integrating, or deploying AI agents, chatbots, intelligent workflows, or automation solutions, ideally using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.
• Strong proficiency in Python, including FastAPI, and familiarity with modern software development practices such as version control, testing, and CI/CD.
• Practical experience with API design and integration, including REST, gRPC, or GraphQL.
• Understanding of machine learning concepts, including model fine‑tuning, embeddings, model evaluation, prompt engineering, LLM operations, and Retrieval‑Augmented Generation (RAG).
• Familiarity with vector databases and retrieval architectures, such as Qdrant, Pinecone, or Weaviate.
• Exposure to cloud platforms and managed AI/ML services, including Azure, AWS, or GCP.
• Understanding of DevOps/MLOps practices, including CI/CD, environment automation, telemetry, and production deployment.
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes is an advantage.
• Strong analytical and problem‑solving skills, with the ability to collaborate effectively with cross‑functional technical teams.
• Knowledge of Responsible AI principles, human‑in‑the‑loop systems, and AI governance best practices is preferred.
• Demonstrated curiosity and willingness to stay updated with emerging agentic AI, orchestration, interoperability frameworks (e.g., MCP), and open‑source AI technologies.

Requirements: