Senior C Python Developer Jobs in Boston, MA (NOW HIRING)

Location: Waltham, MA (Hybrid)

You'll be joining a collaborative, fast-moving team of data scientists, AI engineers, machine learning engineers, and data engineers who work together to tackle complex, high-impact problems at the intersection of AI and enterprise software.

We operate with a genuinely agile mindset — shipping iteratively, challenging assumptions, and staying close to the cutting edge. The team is proactive about research, consistently evaluating and adopting state-of-the-art methodologies, and we encourage everyone to experiment, share findings, and bring new ideas to the table. If you thrive in an environment where intellectual curiosity is the norm and the work is always evolving, you'll fit right in.

SS&C combines proprietary technology with deep industry expertise to support complex financial and health care operations. Our teams design, implement, and operate solutions that help clients manage data, automate processes, and scale their businesses with confidence.

You will work with industry experts, modern platforms, and evolving technologies, gaining exposure to real-world operational challenges and large-scale enterprise environments.

How You Will Make an Impact

• Build and iterate on AI agents end-to-end — from defining goals, personas, and constraints to wiring LLM reasoning with tool execution.
• Design and build end-to-end autonomous AI agents that take user input, reason across multi-step workflows via large language models, and execute actions through APIs, tools, and enterprise data sources.
• Implement planning strategies including chain-of-thought, ReAct loops, and hierarchical task decomposition to enable agents to solve multi-step problems autonomously.
• Design and refine system prompts, few-shot examples, and guardrails that shape agent behavior, tone, and decision-making boundaries
• Implement advanced planning and memory systems — including chain-of-thought loops, ReAct patterns, deep plan and hierarchical planners — that allow agents to decompose complex tasks, retain context, and improve over time using vector database memory.

Terminal-Based AI Coding & Development

• Work extensively inside  AI-powered coding terminals (OpenCode, DeepCode, Hermes Agent) as both a user and a builder — understanding how these tools orchestrate LLM calls, file edits, and shell commands.
• Contribute to the development of  custom coding agent workflows  that automate code generation, review, refactoring, and testing tasks.
• Evaluate and benchmark terminal agent behaviors: accuracy of code edits, hallucination rates, context utilization, and multi-file reasoning.

Model Context Protocol (MCP) & Tool Integration

• Build and extend  MCP servers (using FastMCP and similar frameworks) that expose internal tools, databases, and APIs as structured capabilities for agents.
• Design  tool schemas, descriptions, and invocation patterns  that LLMs can reliably discover and call.
• Integrate agents with external services — REST APIs, vector stores, graph databases, and internal SDKs — through well-defined MCP interfaces.

CI/CD Pipeline

• Containerize and deploy production-grade agent services on Kubernetes, including building CI/CD pipelines, autoscaling configurations, and infrastructure-as-code with Docker and Helm.
• Define evaluation frameworks and observability pipelines (LangFuse) to measure agent performance — covering retrieval accuracy, tool-selection correctness, and hallucination rate — and use insights to drive continuous improvement via A/B testing and audits.
• Embed security and compliance into every layer: IAM token, Connect tokens, prompt injection mitigations, etc.
• Partner closely with data scientists, ML engineers, product managers, and security teams to translate complex business needs into robust, scalable agentic solutions.

LLM Experimentation & Evaluation

• Experiment with a range  of  open-source LLMs (Qwen, DeepSeek, finetune domain-specific models) to evaluate reasoning quality, latency, cost, and tool-use reliability.
• Explore inference optimizations such  as  speculative decoding, constraint decoding, structured outputs, and router-mode orchestration.
• Build and run  evaluation pipelines  to measure retrieval accuracy, tool-selection precision, hallucination rate, and end-to-end task completion.

Memory & Retrieval Systems

• Integrate agents with a broad ecosystem of external systems: vector stores (PgVector, Milvus), relational and graph databases, REST APIs, and internal microservices, all managed through secure, least-privilege access patterns.
• Design and test  memory architectures — short-term (conversation context), long-term (vector-stored interaction history), and episodic (task-specific recall) — to improve agent continuity and personalization.

Required Experience

Strong Python Engineering — Expert-level Python skills across OOP, async programming, testing, and packaging, with the ability to write clean, modular, production-grade code. Familiarity with JavaScript/TypeScript is a plus for UI integrations or edge agent work.

AI & LLM Expertise — A solid foundation in how large language models work, including transformer architecture, tokenization, context windows, and prompting strategies, paired with hands-on experience using LLM APIs from providers like OpenAI, Anthropic, Hugging Face, and Ollama. You understand embeddings, vector similarity search, and RAG pipelines and know how to apply them in production systems.

Agentic AI Development — Proven experience designing and building autonomous AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and Semantic Kernel. You're fluent in agent design patterns — ReAct loops, chain-of-thought planning, hierarchical task decomposition, and multi-agent coordination — and can translate complex business workflows into reliable agentic solutions.

MCP & Tool Integration — Familiarity with Model Context Protocol architecture and the ability to build and extend MCP servers using FastMCP to expose enterprise tools, databases, and APIs as structured, agent-callable capabilities with well-defined schemas and invocation patterns.

Framework-Level Customization & Source Code Engineering — The ability to go beyond standard library usage and work directly at the source code level of AI frameworks. This includes forking and modifying open-source agent libraries, fusing capabilities across frameworks — such as integrating LangGraph's ReAct agent architecture with the deep planning capabilities of OpenCode-style coding agents to produce a hybrid base agent — and packaging those modifications cleanly for team-wide reuse. You know how to navigate unfamiliar codebases quickly, make targeted changes without breaking upstream compatibility, and contribute back where appropriate.

Custom Agent Packaging & CI/CD Deployment — Experience containerizing heavily customized agent builds — including forked or modified third-party libraries — into reproducible Docker images and shipping them through automated CI/CD pipelines (GitHub Actions, Jenkins). You can manage dependency pinning, versioning, and environment parity across dev, staging, and production, ensuring that bespoke framework modifications are treated as first-class, production-grade software rather than one-off hacks.

Memory & Retrieval Systems — Hands-on experience with vector databases (PgVector, Milvus) and the ability to design short-term, long-term, and episodic memory architectures that improve agent continuity and personalization over time.

Data Engineering & Databases — Working knowledge of relational (PostgreSQL), NoSQL (MongoDB), and graph databases (Neo4j), along with experience building ETL pipelines to maintain agent knowledge bases.

DevOps & Deployment — Comfort containerizing and deploying agent services using Docker and Kubernetes, building CI/CD pipelines with Jenkins, and working with cloud ML platforms such as AWS.

Evaluation & Observability — Experience defining and running evaluation frameworks — using tools like LangFuse or custom harnesses — to measure retrieval accuracy, tool-selection precision, hallucination rates, and end-to-end task completion, then using those insights to drive continuous improvement.

Security & Governance — Awareness of secure coding practices, IAM and token-based authentication, prompt injection mitigations, and data encryption, with a mindset of embedding compliance into every layer of agent design.

Curiosity & Learning Agility — A genuine habit of staying at the cutting edge — reading research, experimenting with emerging models, and building side projects. You're comfortable with ambiguity, thrive in fast-moving environments, and bring new ideas to the table without waiting to be asked.

Hands on experience in below stack:

Agent Frameworks (e.g., OpenCode, Claude Code, LangChain, LangGraph, LlamaIndex, CrewAI,